From Expensive CFD to Real-Time Prediction: Data-Driven CFD Acceleration with Machine Learning
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will introduce the use of SmartUQ for CFD applications. Customer use cases from industries including aerospace, automotive, and semiconductor will be used for illustration of the tools and techniques discussed. SmartUQ’s ability to integrate with CFD tools such as ANSYS Fluent, STAR-CCM+, CFD++, and OpenFOAM will also be discussed.
RegisterFrom Expensive CFD to Real-Time Prediction: Data-Driven CFD Acceleration with Machine Learning
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will introduce the use of SmartUQ for CFD applications. Customer use cases from industries including aerospace, automotive, and semiconductor will be used for illustration of the tools and techniques discussed. SmartUQ’s ability to integrate with CFD tools such as ANSYS Fluent, STAR-CCM+, CFD++, and OpenFOAM will also be discussed.
RegisterFrom Expensive CFD to Real-Time Prediction: Data-Driven CFD Acceleration with Machine Learning
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will introduce the use of SmartUQ for CFD applications. Customer use cases from industries including aerospace, automotive, and semiconductor will be used for illustration of the tools and techniques discussed. SmartUQ’s ability to integrate with CFD tools such as ANSYS Fluent, STAR-CCM+, CFD++, and OpenFOAM will also be discussed.
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A Comprehensive Machine Learning Framework for Digital Twins: Data Collection, Surrogate Modeling, and Calibration
Join us for this webinar in which SmartUQ Principal Application Engineer, Gavin Jones, will introduce the use of SmartUQ for Digital Twins. The framework that will be presented applies to all manner of digital twins including design twins, manufacturing twins, and operational twins. The presentation will also include examples from customer use cases and a software demonstration.
RegisterA Comprehensive Machine Learning Framework for Digital Twins: Data Collection, Surrogate Modeling, and Calibration
Join us for this webinar in which SmartUQ Principal Application Engineer, Gavin Jones, will introduce the use of SmartUQ for Digital Twins. The framework that will be presented applies to all manner of digital twins including design twins, manufacturing twins, and operational twins. The presentation will also include examples from customer use cases and a software demonstration.
RegisterA Comprehensive Machine Learning Framework for Digital Twins: Data Collection, Surrogate Modeling, and Calibration
Join us for this webinar in which SmartUQ Principal Application Engineer, Gavin Jones, will introduce the use of SmartUQ for Digital Twins. The framework that will be presented applies to all manner of digital twins including design twins, manufacturing twins, and operational twins. The presentation will also include examples from customer use cases and a software demonstration.
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Gaussian Process vs Deep Learning for Engineering Simulation
In this webinar, SmartUQ’s Principal Application Engineer, Gavin Jones, will discuss the strengths, trade-offs, and practical applications of both GP and deep learning, with real-world examples using SmartUQ.
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SmartUQ Software for Verification, Validation and Uncertainty Quantification of Engineering Simulation
Join us for this webinar, where SmartUQ Principal Application Engineer, Gavin Jones, will showcase SmartUQ’s tools and features for supporting VVUQ efforts. Topics to be covered will include sensitivity analysis, uncertainty propagation, model calibration, and area validation metrics. How the topics discussed relate to important standards documents such as NASA STD 7009A and ASME VVUQ 10, 20, and 40 will also be addressed.
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Modern AI and Machine Learning for Accelerating Aerospace Simulation
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will discuss SmartUQ’s tools for accelerating aerospace simulation including design of experiments (DOE), ML models, statistical calibration, and optimization under uncertainty.
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Design of Experiments, Calibration, and Machine Learning for EV Battery Simulation and Test
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will introduce the use of SmartUQ for EV battery applications. SmartUQ’s ability to integrate with tools commonly used for EV battery simulation such as COMSOL, ANSYS Fluent, STAR-CCM+, MATLAB, and Simulink will also be discussed.
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The Crucial Role of Surrogate Accuracy for Simulation Optimization
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will demonstrate why surrogate model accuracy is so important for indirect optimization success. How SmartUQ addresses the need for accuracy with its best-in-class machine learning models will also be discussed.
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Modern AI and Machine Learning to Accelerate Electric Vehicle Simulations
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will discuss SmartUQ’s tools for accelerating EV simulations including design of experiments (DOEs), ML models, statistical calibration, and optimization under uncertainty. Points will be illustrated with demonstration in SmartUQ. SmartUQ’s ability to integrate with tools commonly used for EV simulation such as Hexagon Adams, COMSOL, ANSYS Fluent, NASTRAN, STAR-CCM+, MATLAB, and Simulink will also be discussed.
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Design of Experiments, Calibration, and Machine Learning for Vehicle Dynamics Simulations
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will introduce the use of SmartUQ for vehicle dynamics applications. SmartUQ’s ability to integrate with tools for vehicle dynamics such as Adams, RecurDyn, and MotionSolve will also be discussed.
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AI and Machine Learning for COMSOL Simulations Webinar Series Part 1: Modern Design of Experiments
In this first webinar of 4, SmartUQ’s modern, efficient design of experiment (DOE) tools for deciding which data to collect for ML model training will be discussed. Topics covered will include DOEs for batch collection of data, to facilitate multi-fidelity modeling approaches, for problems with input constraints, and for simulation model calibration. SmartUQ’s Adaptive DOEs which utilize an existing ML model to intelligently decide where further data should be collected in order to have the largest effect on improving the ML model’s accuracy will also be discussed.
RegisterAI and Machine Learning for ANSYS Simulations Webinar Series Part 1: Modern Design of Experiments
In this first webinar of 4, SmartUQ’s modern, efficient design of experiment (DOE) tools for deciding which data to collect for ML model training will be discussed. Topics covered will include DOEs for batch collection of data, to facilitate multi-fidelity modeling approaches, for problems with input constraints, and for simulation model calibration. SmartUQ’s Adaptive DOEs which utilize an existing ML model to intelligently decide where further data should be collected in order to have the largest effect on improving the ML model’s accuracy will also be discussed.
RegisterDesign of Experiments, Calibration, and Machine Learning for Aerospace CFD Simulations
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will introduce the use of SmartUQ for aerospace CFD applications. SmartUQ’s ability to integrate with CFD tools such as ANSYS Fluent, STAR-CCM+, CFD++, and OpenFOAM will also be discussed.
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Design of Experiments, Calibration, and Machine Learning for Automotive CFD Simulations
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will introduce the use of SmartUQ for automotive CFD applications. SmartUQ’s ability to integrate with CFD tools such as ANSYS Fluent, STAR-CCM+, CFD++, and OpenFOAM will also be discussed.
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AI and Machine Learning for COMSOL Simulations Webinar Series Part 2: Fast, Accurate, Flexible Surrogate Models
The success of surrogate modeling requires fast training speed and high prediction accuracy. Without speed, training a model can become infeasible as the scale and complexity of the problem increases. Without high accuracy a ML model’s predictions will have too much uncertainty to be usable. This 2nd of 4 webinars, will cover how SmartUQ addresses the need for speed and accuracy with its best in class ML models. Further discussed will be how SmartUQ’s accuracy and speed advantages are augmented by a flexible approach featuring many unique ML models, specifically designed to handle cases common to engineering simulation.
RegisterAI and Machine Learning for ANSYS Simulations Webinar Series Part 2: Fast, Accurate, Flexible Surrogate Models
This 2nd of 4 webinars, will cover how SmartUQ addresses the need for speed and accuracy with its best in class ML models. Further discussed will be how SmartUQ’s accuracy and speed advantages are augmented by a flexible approach featuring many unique ML models, specifically designed to handle cases common to engineering simulation.
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Design of Experiments, Calibration, and Machine Learning for Semiconductor Manufacturing CFD Simulations
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will introduce the use of SmartUQ for semiconductor manufacturing CFD applications. SmartUQ’s ability to integrate with CFD tools such as ANSYS Fluent, STAR-CCM+, CFD++, and OpenFOAM will also be discussed.
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AI and Machine Learning for COMSOL Simulations Webinar Series Part 3: Model Calibration
COMSOL simulations and surrogate models of COMSOL simulations are of course only of benefit if the results they produce agree well with physical data, for example in the form of test or experimental data. This 3rd of 4 webinars will cover SmartUQ’s tools for model calibration including unique statistical calibration approaches which address the role that both parameter uncertainty and modeling assumptions play in the disagreement between simulation results and physical data.
RegisterAI and Machine Learning for ANSYS Simulations Webinar Series Part 3: Model Calibration
ANSYS simulations and surrogate models of ANSYS simulations are of course only of benefit if the results they produce agree well with physical data, for example in the form of test or experimental data. This 3rd of 4 webinars will cover SmartUQ’s tools for model calibration including unique statistical calibration approaches which address the role that both parameter uncertainty and modeling assumptions play in the disagreement between simulation results and physical data.
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AI and Machine Learning for COMSOL Simulations Webinar Series Part 4: Sensitivity Analysis
In this last of 4 webinars the use of SmartUQ’s sensitivity analysis tools for gaining insights into COMSOL simulations will be discussed. Demonstrations of sensitivity analysis in SmartUQ will illustrate applications including assessing how robust the design or process being modeled is to uncertainty and which COMSOL model inputs are the greatest drivers of uncertainty in the outputs.
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AI and Machine Learning for ANSYS Simulations Webinar Series Part 4: Sensitivity Analysis
In this last of 4 webinars the use of SmartUQ’s sensitivity analysis tools for gaining insights into ANSYS simulations will be discussed. Demonstrations of sensitivity analysis in SmartUQ will illustrate applications including assessing how robust the design or process being modeled is to uncertainty and which ANSYS model inputs are the greatest drivers of uncertainty in the outputs.
RegisterDesign of Experiments, Calibration, and Machine Learning for RecurDyn Simulations
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will introduce the use of SmartUQ for RecurDyn simulation applications. SmartUQ’s ability to integrate with RecurDyn will also be demonstrated and discussed.
RegisterDesign of Experiments, Calibration, and Machine Learning for Particleworks Simulations
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will introduce the use of SmartUQ for Particleworks simulation applications. SmartUQ’s ability to integrate with Particleworks will also be demonstrated and discussed.
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Modern Design of Experiments, Data Sampling, and Machine Learning for Engineering Test Data
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will discuss SmartUQ’s tools for collecting, modeling, and analyzing test data. The unique strengths and capabilities of SmartUQ’s tools will be highlighted along with a software demonstration and examples from customer use cases.
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Design of Experiments, Calibration, and Machine Learning for Gas Turbine Simulations
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will introduce the use of SmartUQ for gas turbine applications. The unique strengths and capabilities of SmartUQ’s tools will be highlighted along with a software demonstration and examples from customer use cases. SmartUQ’s ability to integrate with tools such as ANSYS Fluent, ABAQUS, and STAR-CCM+ will also be discussed.
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Coupling SmartUQ's Predictive Analytics Solutions to COMSOL Multiphysics
This webinar will show how SmartUQ software can enhance product development and design exploration activities in COMSOL through the application of predictive analytics and UQ techniques. Using a NACA airfoil CFD simulation for demonstration, this webinar will walk through SmartUQ’s analytics workflow coupled to COMSOL. The webinar will also highlight additional applications of SmartUQ to other COMSOL simulations.
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SmartUQ Capabilities and Strengths: A COMSOL UQ Module Comparison
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will highlight the unique strengths and capabilities of SmartUQ using the COMSOL UQ Module for comparison. Topics discussed will include SmartUQ’s design of experiments (DOEs), machine learning (aka surrogate) models, statistical calibration, inverse analysis tools, and data sampling of existing data sets.
RegisterMachine Learning for Closing the Virtual-Physical Gap in Digital Twins
Join us for his webinar in which SmartUQ principal application engineer, Gavin Jones, will introduce SmartUQ’s statistical calibration tools in the context of digital twin applications.
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Predictive Analytics for the Digital Twin: An Electric Motor Use Case
The audience for this webinar includes engineers, managers, and data scientists in both industrial and defense sectors who are involved in simulation, experimental testing, design, and analyses and have interest in learning more about using analytics for the digital twin.
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SmartUQ Mini Demo Webinar: Lightweighting a Bracket Simulation Model with Uncertainty Quantification
This demonstration webinar will illustrate how the UQ toolset in the SmartUQ software determines the geometric parameters to minimize the bracket’s weight subject to fatigue life and maximum displacement under loading constraints.
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SmartUQ Mini Demo Webinar: Predictive Analytics and Uncertainty Quantification of a Microscale Porous Reactor Simulation
This webinar will illustrate how the analytics tools in SmartUQ software can quantify uncertainties and provide valuable insights for a simulation model of a microscale porous reactor. In addition, SmartUQ is used to determine the optimal geometric parameters for a concentration at the outlet of the reactor.
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SmartUQ Mini Demo Webinar: Statistical Calibration of a Balloon Expandable Coronary Stent Simulation
Using the same workflow as suggested in the ASME V&V 10 and V&V 40 documentation, Uncertainty Quantification methods, such as statistical calibration and uncertainty propagation, will be applied to a practical, end-to-end case study in which the performance of a balloon expandable coronary stent model is analyzed with respect to geometric and material property variation. The model’s credibility will also be assessed using a set of mock physical test results.
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The Blessings of Predictive Models for Fast-running Simulations
Even with quick simulation models, performing advanced analytics tasks like design space exploration, sensitivity analysis, uncertainty propagation, and optimization are time consuming and not practical. But a predictive model can rapidly predict the outputs for input configurations not contained in the training data, making it an excellent option for performing advanced analytics tasks that require a number of function evaluations. Moreover, predictive models are necessary to improve a simulation model’s accuracy and estimate the distribution of inputs by using statistical calibration and inverse analysis techniques, respectively. All these methodologies will be discussed in this webinar.
RegisterSmartUQ Software Tutorial: Demonstrating Modern Machine Learning for the Manufacturing Industry
This 90-minute tutorial will provide an introduction to the basics of ML. Attendees will be shown through the use of example problems how to use SmartUQ’s most popular ML tools for solving engineering problems. This will include tools for space filling Design of Experiments, data sampling, predictive modeling, sensitivity analysis, uncertainty propagation, and statistical calibration. Applications of SmartUQ tools for purposes such as acceleration of simulation efforts, reduction of testing requirements, decision making under uncertainty, virtual sensors, and digital twins will be discussed. Attendees will learn about the challenges of ML in engineering problem solving and how to address these problems using SmartUQ tools such as those for active learning and adaptive design.
RegisterSmartUQ Software Tutorial: Demonstrating Machine Learning for Narrowing the Simulation-Test Gap
This tutorial is interactive. The audience are encouraged to ask questions during the tutorial.
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Machine Learning for Sensitivity Analysis of Engineering Simulations
This webinar will provide an introduction to both sampling and predictive modeling based sensitivity analysis. Examples will be used to highlight the benefits of a machine learning approach when dealing with complex real-world applications.
RegisterArtificial Intelligence and Machine Learning for Semiconductor Manufacturing
- Anticipate variation from mechanical, thermal, electrical, optical, and chemical processes to improve stability and decrease error rates. - Optimize under uncertainty to improve expected performance. - Accelerate reliability assessment and design for novel applications. - Analyze equipment records and create simulation or empirical digital twins to maximize uptime and quality. - Tune and calibrate physics-based simulations, control models, and machines to recorded data.
RegisterArtificial Intelligence and Machine Learning for Semiconductor Design
Join us for this webinar to learn how artificial intelligence and machine learning can be used to improve the semiconductor design process.
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Artificial Intelligence and Machine Learning for Automotive Applications
Join us for this webinar to learn how AI and machine learning models can be used to enhance automotive design and manufacturing applications.
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Machine Learning for Functional Response Prediction: SmartUQ’s Functional Response Emulator
SmartUQ currently offers a Functional Response Emulator (An emulator is a predictive model) capable of handling such problems. Join us for this webinar to learn more and see a demo.
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Machine Learning for Spatial and Temporal Response Prediction: SmartUQ’s Spatial/Temporal Emulator and Varying Geometry Module
SmartUQ features a number of predictive models called emulators. Join us for this webinar to learn more about SmartUQ’s Spatial/Temporal Emulator and Varying Geometry module.
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Machine Learning for Prediction of High Dimensional Systems: SmartUQ’s Active Dimension Hybrid Emulator
SmartUQ has developed a unique GP based model, the Active Dimension Hybrid Emulator (An emulator is a predictive model), specifically designed for efficiently training problems with large numbers of inputs. Join us for this webinar to learn more and see a demo.
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Machine Learning for Prediction of Systems with Discontinuous Response: SmartUQ’s Mixed Input Classification Emulator
SmartUQ has developed a unique GP based model, the Mixed Input Classification Emulator (An emulator is a predictive model), specifically designed for problems with discrete response behavior such as discontinuities. Join us for this webinar to learn more and see a demo.
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SmartUQ’s Best in Class Gaussian Process Models for Simulations
Join us for this free webinar in which SmartUQ principal application engineer, Gavin Jones, will introduce SmartUQ’s GP models and present more detailed results of the benchmarks. If interested in a trial version of SmartUQ to see for yourself, please contact [email protected].
RegisterUncertainty Quantification for Simulation Modeling in the Medical Device Industry
Additionally, using SmartUQ software for illustration, these concepts will be applied to a practical, end-to-end case study in which the performance of a balloon expandable coronary stent model is analyzed with respect to geometric and material property variation. The model’s credibility will also be assessed using a set of mock physical test results.
RegisterSmartUQ Workshop: Accurate, Comprehensive Machine Learning Software for Engineering Simulation and Digital Twin Applications
The Workshop will be 90 minutes in total, with 60 minutes of presentation. The remaining 30 minutes will be for audience Q&A both during and at the conclusion of the presentation. Attendees are encouraged to come prepared with any of their own questions on the use of Artificial Intelligence and ML.
RegisterFast, Accurate, and Comprehensive Machine Learning Software for Aerospace and Defense Simulations and Digital Twins
SmartUQ is a fast, accurate, and comprehensive Machine Learning (ML) and Uncertainty Quantification (UQ) software tool optimally designed for simulation, digital twin, and other engineering applications. SmartUQ includes best in class ML models, which significantly outperform the competition in terms of training speed and predictive accuracy. SmartUQ also features statistical calibration tools for achieving better agreement between simulation and physical data.
RegisterFast, Accurate, and Comprehensive Machine Learning for Automotive Simulations and Digital Twins
SmartUQ customer use cases from the automotive industry will also be presented to illustrate the real-world impact SmartUQ can have on solving simulation and digital twin related engineering problems. These successes have saved SmartUQ customers millions of dollars and thousands of hours of work.
RegisterDesign of Experiments, Validation, and Machine Learning for Structural Simulations
The webinar will include a live demonstration of SmartUQ, example customer use cases, and end with a Q&A session.
RegisterSmartUQ Machine Learning Software Complements JMP (Pro) Software
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will highlight the unique strengths and capabilities of SmartUQ that complement JMP (Pro). Topics to be discussed will include SmartUQ’s modern DOEs, ML models, and tools for statistical calibration, inverse analysis, and data sampling. The significant speed, accuracy, and variety advantages of SmartUQ’s Gaussian process (GP) models as compared to the GP modeling offered in JMP and JMP Pro will also be covered.
RegisterModern Design of Experiments, Model Validation, and Machine Learning Methods for Pharmaceutical Simulations
The webinar will include a live demonstration of SmartUQ and end with a Q&A session.
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Modern Design of Experiments and Data Sampling for Simulation, Test, and Digital Twins
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will discuss SmartUQ’s design of experiment (DOE) and data sampling tools. The unique strengths and capabilities of SmartUQ’s tools will be highlighted along with a software demonstration and examples from customer use cases.
RegisterArtificial Intelligence and Machine Learning for Aerospace Simulation
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will discuss SmartUQ’s tools for benefiting simulation including design of experiments (DOE), machine learning models, statistical calibration, and optimization under uncertainty. A demonstration of SmartUQ making use of data from a simulation of an Ares launch vehicle will be included in the presentation.
RegisterSmartUQ Machine Learning Software Complements ModeFRONTIER
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will highlight the unique strengths and capabilities of SmartUQ that complement ModeFRONTIER. The significant speed, accuracy, and variety advantages of SmartUQ’s ML models as compared to those offered in ModeFRONTIER will also be covered.
RegisterIntroduction to SmartUQ AI and Uncertainty Quantification Software
Join our webinar with SmartUQ's principal application engineer, Gavin Jones, for an introduction to SmartUQ. Discover its unique strengths, including efficient design of experiments (DOEs), fast and accurate machine learning models, statistical calibration tools, and optimization under uncertainty. The webinar will feature demonstrations and customer success stories from various industries.
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Machine Learning for Narrowing the Simulation-Test Gap: SmartUQ’s Data Matching and Bayesian Calibration
From each tool a resulting machine learning model of the simulation can even be used directly for prediction allowing for rapid analyses, that wouldn’t be possible via direct evaluation of the simulation itself.
RegisterDesign of Experiments, Calibration, and Machine Learning for Multibody Dynamics Simulations
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will introduce the use of SmartUQ for multibody dynamics applications. SmartUQ’s ability to integrate with multibody dynamics tools such as Adams, RecurDyn, and Dymola will also be discussed.
RegisterSmartUQ Machine Learning Software Complements ANSYS OptiSLang
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will highlight the unique strengths and capabilities of SmartUQ that complement ANSYS OptiSLang. The significant speed, accuracy, and variety advantages of SmartUQ’s ML models as compared to those offered in ANSYS OptiSLang will also be covered.
RegisterDesign of Experiments, Calibration, and Machine Learning for Materials Simulation
AI and Machine Learning (ML) tools including design of experiments, predictive ML models, and statistical calibration can help by significantly reducing the computational cost of simulation, achieving more accurate simulation, and providing a systematic approach to managing uncertainty. Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will introduce the use of SmartUQ AI and Machine Learning software for materials simulation applications.
RegisterSmartUQ Machine Learning Software Complements Siemens Heeds
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will highlight the unique strengths and capabilities of SmartUQ that complement Siemens Heeds. The significant speed, accuracy, and variety advantages of SmartUQ’s ML models as compared to those offered in Siemens Heeds will also be covered.
RegisterSmartUQ Machine Learning Software Complements Hexagon ODYSSEE
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will highlight the unique strengths and capabilities of SmartUQ that complement ODYSSEE. The significant speed, accuracy, and variety advantages of SmartUQ’s ML models as compared to those offered in ODYSSEE will also be covered.
RegisterSmartUQ Machine Learning Software Complements Altair RAMDO
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will highlight the unique strengths and capabilities of SmartUQ that complement RAMDO. The significant speed, accuracy, and variety advantages of SmartUQ’s ML models as compared to those offered in RAMDO will also be covered.
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Modern Machine Learning for Jet Engine and Gas Turbine Applications
The first half of this presentation will provide an introduction to SmartUQ software, including a live demonstration. This will be followed by a discussion of how SmartUQ can be applied to jet engine and gas turbine applications such as high and low pressure compressor, combustor, and fan module applications. The session will conclude with Q&A.
RegisterDesign of Experiments, Calibration, and Machine Learning for Multi-Fidelity Simulation and Data Fusion
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will introduce the use of SmartUQ design of experiments, machine learning models, and unique statistical calibration approaches for multi-fidelity modeling and data fusion.
RegisterIntegrating SmartUQ’s Design of Experiments, Machine Learning, and Calibration tools with MATLAB and Simulink
Join SmartUQ Principal Application Engineer Gavin Jones for this webinar to learn how the power of SmartUQ’s best in class tools can be integrated with MATLAB scripts and Simulink workflows. The webinar will feature an overview of SmartUQ as well as software demonstrations.
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Design of Experiments, Calibration, and Machine Learning for Electronics Simulations
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will introduce the use of SmartUQ for electronics simulation applications. SmartUQ’s ability to integrate with simulation tools such as offered by ANSYS and COMSOL will also be discussed.
RegisterAccelerating Simulation Optimization with Surrogate Modeling
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will introduce surrogate modeling and its benefits to optimization workflows. The session will cover how surrogate models are trained, validated, and used to efficiently explore design spaces and identify optimal solutions with fewer simulation runs.
RegisterModern Artificial Intelligence and Machine Learning to Accelerate Simulation for Aerospace Startups
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will discuss SmartUQ’s tools for accelerating simulations including design of experiments (DOE), ML models, statistical calibration, and optimization under uncertainty.
RegisterSmartUQ Free Training – Fast & Accurate AI and Machine Learning Software Intro for Engineering and Manufacturing Companies
No prior advanced statistics background or machine learning experience is required, the training will be equally valuable to engineers, scientists, and data scientists.
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Transforming Tire Simulation with Modern Machine Learning
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will introduce the use of SmartUQ for tire simulation applications. SmartUQ’s ability to integrate with simulation tools such as offered by ANSYS and COMSOL will also be discussed.
RegisterCutting Edge Machine Learning Software Webinar: Introduction to SmartUQ 11.0 Release
This webinar will demonstrate the new features within SmartUQ software and conclude with a Q&A session.
RegisterModern AI and Machine Learning for Accelerating Aerospace Simulation
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will discuss SmartUQ’s tools for accelerating aerospace simulation including design of experiments (DOE), ML models, statistical calibration, and optimization under uncertainty.
RegisterModern AI and Machine Learning to Accelerate Semiconductor Equipment Simulations
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will discuss SmartUQ’s tools for accelerating semiconductor simulations including design of experiments (DOEs), ML models, statistical calibration, and optimization under uncertainty. Points will be illustrated with demonstration in SmartUQ and customer use cases.
RegisterDesign of Experiments, Calibration, and Machine Learning for Hexagon Adams Simulations
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will introduce the use of SmartUQ for Adams simulation applications. SmartUQ’s ability to integrate with Adams will also be demonstrated and discussed.
RegisterFast, Accurate, and Flexible Surrogate Models for Engineering Simulation
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will provide an introduction to SmartUQ’s fast, accurate, and flexible surrogate models.
RegisterDesign of Experiments, Calibration, and Machine Learning for CFD Simulations
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will introduce the use of SmartUQ for CFD applications. SmartUQ’s ability to integrate with CFD tools such as ANSYS Fluent, STAR-CCM+, CFD++, and OpenFOAM will also be discussed.
RegisterModern AI and Machine Learning to Accelerate Automotive Simulations
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will discuss SmartUQ’s tools for accelerating automotive simulations including design of experiments (DOEs), ML models, statistical calibration, and optimization under uncertainty. Points will be illustrated with demonstration in SmartUQ and customer use cases.
RegisterSmartUQ Machine Learning Software Complements Optimizations Tools: A Comparison of SmartUQ to OptiSLang, ModeFRONTIER, and Heeds
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will highlight the unique strengths and capabilities of SmartUQ that complement optiSLang, ModeFRONTIER, and Heeds. The significant speed, accuracy, and variety advantages of SmartUQ’s ML models as compared to those offered in optiSLang, ModeFRONTIER, and Heeds will also be covered.
RegisterSmartUQ Machine Learning Software Complements Dassault Isight
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will highlight the unique strengths and capabilities of SmartUQ that complement Isight. The significant speed, accuracy, and variety advantages of SmartUQ’s ML models as compared to those offered in Isight will also be covered.
RegisterFast, Accurate Bayesian Optimization for Engineering Simulation and Experimentation
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will introduce SmartUQ’s tools for Bayesian Optimization. The superior speed and accuracy of SmartUQ’s Bayesian Optimization will be covered through benchmarks and example problems.
RegisterModern Design of Experiments, Machine Learning, and Calibration for Simulation and Digital Twins
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will introduce the use of SmartUQ for simulation and digital twin applications. A demonstration of SmartUQ as well as discussion of customer use cases will be included as part of the presentation.
RegisterModern Design of Experiments, Machine Learning, and Calibration for COMSOL Simulation and Digital Twins
Join us for this webinar in which SmartUQ Principal Application Engineer, Gavin Jones, will introduce the use of SmartUQ for COMSOL simulation and digital twin applications. Customer use cases and SmartUQ’s ability to integrate with COMSOL will also be demonstrated and discussed.
RegisterFrom Taguchi Methods to Uncertainty Quantification for Simulation Users
Join us for this webinar, where SmartUQ’s Principal Application Engineer, Gavin Jones, will showcase SmartUQ’s tools for integrating Taguchi methods with UQ and explore capabilities in space-filling designs, machine learning, optimization under uncertainty, and simulation model calibration.
RegisterAccelerating ANSYS Simulation and Digital Twins with Modern Machine Learning
Join us for this webinar in which SmartUQ Principal Application Engineer, Gavin Jones, will introduce the use of SmartUQ for ANSYS simulation and digital twin applications. Customer use cases and SmartUQ’s ability to integrate with ANSYS will also be demonstrated and discussed.
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Digital Engineering Fundamentals
In this 8-hour course, students will gain an in-depth understanding of Digital Engineering tools, principles, and practices. They will learn to think digitally, not digitized, and to focus on DE to transform lifecycle processes to deliver knowledge enabling better decision making at the speed of relevance.
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Machine Learning for Quantifying Uncertainties in Engineering Applications
Hosted by AIAA, This course will provide an introduction to ML, with particular focus on those tools and techniques required for UQ.
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