News – UQ-SciML Group

2023

(2023/10) Siddhant Singh gave a presentation “Tools for Parameterization of a Single Particle Model with Electrolyte Dynamics (SPMe) with Bayesian Experimental Design to Capture Degradation-Induced Capacity Fade in Lithium-Ion Batteries” at the ECS Meeting.
(2023/09) Chengyang Huang gave a presentation “FP-IRL: Fokker-Planck-based Inverse Reinforcement Learning — A Physics-Constrained Approach to Modeling Markov Decision Processes” at MMLDE-CSET.
(2023/09) Yossi Cohen presented a poster “Sparse Modeling of Wavelet Features for Fault Classification and Regression in Spacecraft Propulsion Systems” at PHMAP.
(2023/08) Our paper “A rotorcraft in-flight ice detection framework using computational aeroacoustics and Bayesian neural networks” is published in Structural and Multidisciplinary Optimization.
(2023/08) Our MURI project has been funded by the ONR! (More: 1, 2)
(2023/08) Congratulations to Chengyang Huang for receiving the Michigan Institute for Computational Discovery and Engineering (MICDE) Graduate Fellowship!
(2023/08) Our paper “Bayesian Sequential Optimal Experimental Design for Nonlinear Models Using Policy Gradient Reinforcement Learning” is published in Computer Methods in Applied Mechanics and Engineering.
(2023/08) Our paper “Fault Prognosis of Turbofan Engines: Eventual Failure Prediction and Remaining Useful Life Estimation” is published in International Journal of Prognostics and Health Management.
(2023/08) Congratulations to Zhiyi Chen for successfully defending his Ph.D. thesis!
(2023/08) Hongfan Chen gave a presentation “Model Calibration for Ensemble CME Simulation with the SWMF” at ICIAM.
(2023/08) Aniket Jivani presented a poster “Towards Uncertainty Quantification for Synthetic White Light Images in the Space Weather Modeling Framework” at the SHINE 2023 Workshop.
(2023/08) Learning about UQ at the USC UQ Summer School.
(2023/07) Celebrating Wanggang’s defense with great food at ShiangMi!
(2023/07) Congratulations to Wanggang Shen for successfully defending his Ph.D. thesis!
(2023/07) Xun Huan instructed at the MIDAS Environmental Data Science Summer Academy.
(2023/07) USNCCM
- Thomas Coons gave a presentation “A Multi-Fidelity Approach to Bayesian Optimal Experimental Design”.
- Zhiyi Chen gave a presentation “Data-Centric Modeling of Multistage Manufacturing Systems: A Deep Koopman Approach”.
- Yossi Cohen gave a presentation “Explainable Artificial Intelligence for Smart Manufacturing: Potential and Computational Challenges”.
- Chengyang Huang gave a presentation “Leveraging Fokker-Planck-Based Inverse Reinforcement Learning to Study Living-Agent in Biological Systems”.
- Aniket Jivani gave a presentation “Uncertainty Quantification for Random Field Quantities Using Multifidelity Karhunen-Loève Expansions With Active Learning”.
- Yossi Cohen and Xun Huan co-organized a minisymposium “Machine Learning and Model-Based Dimension Reduction for Exploration and Interpretation of Dynamical and Industrial Systems”.
(2023/07) Daniel Cooper gave a presentation “Expert Elicitation and Data Noise Learning for Material Flow Analysis using Bayesian Inference” at the ISIE2023.
(2023/06) Our preprint “Variational Sequential Optimal Experimental Design using Reinforcement Learning” is available on arXiv.
(2023/06) Our preprint “FP-IRL: Fokker-Planck-based Inverse Reinforcement Learning — A Physics-Constrained Approach to Markov Decision Processes” is available on arXiv.
(2023/06) Our paper “Understanding capacity fade in organic redox-flow batteries by combining spectroscopy with statistical inference techniques” is published in Nature Communications.
(2023/06) Jiankan Liao gave a presentation “Bayesian Model Selection for Network Discrimination in Material Flow Analysis” at ISSST.
(2023/06) Beckett Zhou gave a presentation “Multi-Fidelity Propeller Noise Prediction using a Data-Driven Approach” at the AIAA Aviation Forum. [paper]
(2023/06) Xun Huan gave a presentation “Bayesian Sequential Optimal Experimental Design” at the University of Toronto Insitute for Aerospace Studies Seminar Series.
(2023/05) Our paper “Expert Elicitation and Data Noise Learning for Material Flow Analysis using Bayesian Inference” is published in Journal of Industrial Ecology.
(2023/05) Jiankan Liao gave a presentation “A Bayesian Approach to Modeling Unit Manufacturing Process Environmental Impacts using Limited Data with Case Studies on Laser Power Bed Fusion Cumulative Energy Demand” at CIRP. [paper]
(2023/05) ICODOE
- Thomas Coons gave a presentation “A Multi-Fidelity Approach to Bayesian Optimal Experimental Design”.
- Jiayuan Dong gave a presentation “Variational Bayesian Optimal Experimental Design with Normalizing Flows”.
- Xun Huan gave a presentation “Variational Bayesian Sequential Optimal Experimental Design via Policy Gradient”.
- Xun Huan organized a session “Variational Methods for Optimal Experimental Design”.
(2023/04) Tahera Hossain and Wanggang Shen gave a presentation “A Bayesian Approach for Quantifying Data Scarcity when Modeling Human Behavior via Inverse Reinforcement Learning” at CHI 2023.
(2023/03) Congratulations to Thomas Coons for winning the NSF Graduate Research Fellowships Program (GRFP) award!
(2023/03) Congratulations to Hongfan Chen for successfully passing the Ph.D. qualifying exam!
(2023/03) Congratulations to Maria Han Veiga for accepting a tenure-track postion at the Ohio State University!
(2023/03) Our preprint “Shapley-based Explainable AI for Clustering Applications in Fault Diagnosis and Prognosis” is available on arXiv.
(2023/03) Our paper “Understanding Uncertainty: How Lay Decision-makers Perceive and Interpret Uncertainty in Human-AI Decision Making” is published in Proceedings of the 28th International Conference on Intelligent User Interfaces (IUI ’23).
(2023/03) Xun Huan gave a presentation “A Reinforcement Learning Framework for Bayesian Sequential Experimental Design” at the National University of Singapore Centre for Data Science and Machine Learning (CDSML) Seminar.
(2023/03) Snehal Prabhudesai gave a presentation “Understanding Uncertainty: How Lay Decision-makers Perceive and Interpret Uncertainty in Human-AI Decision Making” at IUI 2023.
(2023/03) Sanat Modak gave a presentation “Understanding capacity fade in organic redox-flow batteries by combining spectroscopy with statistical inference techniques” at ACS Spring 2023.
(2023/03) SWQU Spring Meeting
- Hongfan Chen presented a poster “Connecting White Light Images with In-situ Observations of Solar Wind Quantities for CME Simulations in the SWMF”.
- Aniket Jivani presented a poster “Uncertainty Quantification & Global Sensitivity Analysis for CME Simulations in the SWMF”.
(2023/03) Jatan Buch gave a presentation “Learning fire spread dynamics with physics-constrained machine learning” at the APS March Meeting.
(2023/02) Our paper “Lowering the computational barrier: Partially Bayesian neural networks for transparency in medical imaging AI” is published in Frontiers in Computer Science.
(2023/02) SIAM CSE
- Atlanta Chakraborty gave a presentation “An Approximate Bayesian Computation Approach to Goal-Oriented Bayesian Experimental Design”.
- Thomas Coons gave a presentation “A Multifidelity Approach to Model-Based Bayesian Optimal Experimental Design”.
- Jiayuan Dong gave a presentation “Variational Bayesian Optimal Experimental Design with Normalizing Flows”.
- Jeremiah Hauth gave a presentation “Stein VI for Bayesian Neural Network Prediction of Helicopter Rotor Performance”.
- Xun Huan gave a presentation “Reinforcement Learning Framework for Bayesian Sequential Optimal Experimental Design”.
- Chengyang Huang gave a presentation “Inverse Reinforcement Learning Via Variantioanl System Identification of Fokker-Planck Equation”.
- Aniket Jivani gave a presentation “Uncertainty Quantification for Random Field Quantities Using Multi-Fidelity Karhunen-Loève Expansions with Active Learning”.
- Codie Kawaguchi gave a presentation “Quantifying and Reducing Uncertainty in Inertial Confinement Fusion Experiments Using Optimal Experimental Design”.
- Snehal Prabhudesai gave a presentation “Lowering The Computational Barrier : Selective Bayesian Uncertainty Quantification for Transparency in Medical Imaging AI”.
- Xun Huan co-organized a two-part minisymposium “Model-Based Optimal Experimental Design” (parts I, II).
(2023/01) Our paper “Global Sensitivity Analysis and Uncertainty Quantification for Background Solar Wind using the Alfvén Wave Solar Atmosphere Model” is published in Space Weather.
(2023/01) Celebrating Lunar New Year and Yossi’s graduation with tasty dishes at Hong Hua!
(2023/01) Congratulations to Yossi Cohen for receiving the Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship!
(2023/01) Congratulations to Yossi Cohen for successfully defending his Ph.D. thesis!
(2023/01) Chengyang Huang presented a poster “Leveraging Artificial Intelligence to Understand Single Cell State Transitions and Predict Cell State” at the BMES CMBE Conference.

2022

(2022/12) Yummy food at Namaste Flavours!
(2022/12) AGU Fall Meeting
- Hongfan Chen gave a presentation “Connecting White Light Images with In-situ Observations of Solar Wind Quantities for CME Simulations in the SWMF”.
- Aniket Jivani presented a poster “Uncertainty Quantification and Global Sensitivity Analysis for CME simulations in the SWMF”.
- Gabor Toth presented a poster “Developing the Next Generation Space Weather Modeling Framework: Year 2”.
(2022/11) Xun Huan instructed at the MIDAS Data Science Bootcamp for Environmental Science.
(2022/11) Congratulations to Codie Kawaguchi, Shuyu Long, and Thomas Coons for successfully passing the Ph.D. Qualifying Exam!
(2022/10) Codie Kawaghuchi presented a poster “Quantifying and reducing uncertainty in ICF experiments using optimal experimental design” at the APS Plasma Physics Meeting.
(2022/10) Xun Huan gave a presentation “Goal-Oriented Optimal Experimental Design for Nonlinear Physical Systems” at SES Annual Technical Meeting.
(2022/09) Xun Huan gave a presentation “Bayesian Reinforcement Learning for Optimal Sensor Relocation in Convection-Diffusion Fields” at SIAM MDS.
(2022/08) Xun Huan gave a presentation “Multifidelity Karhunen-Loeve Expansion Surrogate Models for Uncertainty Propagation” at the Joint Statistical Meetings.
(2022/08) WCCM-APCOM Congress
- Siddhartha Srivastava gave a presetation “Discovery of Cell Migration Models by Data Driven Variational System Identification and Inverse Reinforcement Learning”.
- Christian Jacobsen gave a presetation “Variational Bayesian optimal experimental design for the discovery of electro-deposition process models”.
(2022/07) Our paper “Multi-fidelity uncertainty quantification of particle deposition in turbulent pipe flow” is published in Journal of Aerosol Science.
(2022/07) Our paper “A Bayesian Approach for Quantifying Data Scarcity when Modeling Human Behavior via Inverse Reinforcement Learning” is published in ACM Transactions on Computer-Human Interaction.
(2022/06) Daniel Cooper gave a presentation “Expert Elicitation and Data Noise Learning for Material Flow Analysis using Bayesian Inference” at ISSST 2022.
(2022/06) Beckett Zhou gave a presentation “A Data-Driven Approach for Enhancement of Propeller Performance Prediction” at the AIAA/CEAS Aeroacoustics Conference. [paper]
(2022/06) Xun Huan presented a poster “Goal-Oriented Optimal Experimental Design for Nonlinear Systems” at the ISBA World Meeting.
(2022/06) Aniket Jivani presented a poster “Global Sensitivity Analysis for Solar Wind Simulations in the Space Weather Modeling Framework” at the SHINE 2022 Workshop.
(2022/05) Sanat Modak gave a presentation “Understanding Capacity Fade in Organic Redox-Flow Batteries Using Uncertainty Quantification Techniques and Zero-Dimensional Modeling” at the ECS Meeting.
(2022/05) End-of-semester celebration at the Grove!
(2022/04) SIAM UQ
- Andrew Davis gave a presentation “Local Approximation of Expected Utility Surface for Nonlinear Bayesian Optimal Experimental Design”.
- Jiayuan Dong gave a presentation “Expert Elicitation and Opinion Aggregation for Bayesian Prior Construction with Application to the U.S. Steel Flow Analysis”.
- Jeremiah Hauth gave a presentation “Efficient Large-Scale Bayesian Inference for Predictive Modeling in Precision Health Balance Training”.
- Xun Huan gave a presentation “Goal-Oriented Optimal Experimental Design for Nonlinear Models using MCMC”.
- Chengyang Huang gave a presentation “Fast Approximate Bayesian Uncertainty Quantification Using Conditional Generative Adversarial Networks”.
- Aniket Jivani gave a presentation “Sobol’ Sensitivity for Uncertain Model Parameters in Simulations of Background Solar Wind”.
- Snehal Prabhudesai gave a presentation “Partially Bayesian Neural Networks: Low-Cost Bayesian Uncertainty Quantification for Deep Learning in Medical Image Segmentation”.
- Wanggang Shen gave a presentation “Optimal Bayesian Design of Sequential Experiments Using Deep Deterministic Policy Gradient”.
- Maria Han Veiga gave a presentation “Optimal Experimental Design Using Variational Inference Approximations”.
- Xun Huan co-organized a three-part minisymposium “Model-Based Optimal Experimental Design” (parts I, II, III).
(2022/03) Patrick Kinnunen and Lucy Spicher are presentating at the MIDAS PODS Showcase.
(2022/02) A fun group dinner at Cafe Zola!
(2022/01) Xun Huan gave a presentation “Optimal Bayesian Design of Finitely Sequential Experiments with Deep Deterministic Policy Gradient” at the Royal Statistical Society (RSS) Computational Statistics and Machine Learning session on Automatic Experimentation.
(2022/01) Our new project has been funded by the W. M. Keck Foundation! (More: 1, 2)
(2022/01) Xun Huan gave a presentation “Bayesian Recurrent Neural Networks for Monitoring Rotorcraft Icing from Aeroacoustics Time-Series Data” at the AIAA SciTech Forum. [paper]

2021

(2021/12) Xun Huan gave a presentation “Bayesian Sequential Optimal Experimental Design for Nonlinear Systems via Policy Gradient” at the Applied Reinforcement Learning Seminar.
(2021/12) AGU Fall Meeting
- Gabor Toth gave a presentation “Developing the Michigan Sun-to-Earth Model with Data Assimilation and Quantified Uncertainty”.
- Aniket Jivani presented a poster “Global Sensitivity Analysis for Solar-Wind Simulations in the Space Weather Modelling Framework”.
(2021/11) Congratulations to Jiayuan Dong for successfully passing the Ph.D. Qualifying Exam!
(2021/11) Codie Kawaghuchi presented a poster “Using data science methods to efficiently and effectively plan HED experiments and analyze data” at the APS Plasma Physics Meeting.
(2021/11) Xun Huan gave a presentation “Reinforcement Learning for Sequential Optimal Experimental Design” at the New England Statistical Society (NESS) NextGen: Data Science Day.
(2021/10) Our paper “Stratification by Tumor Grade Groups in a Holistic Evaluation of Machine Learning for Brain Tumor Segmentation” is published in Frontiers in Neuroscience.
(2021/10) Xun Huan gave a presentation “Bayesian Optimal Experimental Design for Batch and Sequential Experiments” at the Dartmouth College Applied and Computational Mathematics Seminar.
(2021/08) Congratulations to Chengyang Huang for successfully passing the Ph.D. Qualifying Exam!
(2021/08) Congratulations to Saibal De for successfully defending his Ph.D. thesis!
(2021/08) Our paper “Bayesian Inference of Parameters in Power System Dynamic Model Using Trajectory Sensitivities” is published in IEEE Transactions on Power Systems.
(2021/08) Xun Huan gave a presentation “Closed-Loop Bayesian Design of Sequential Experiments via Dynamic Programming and Reinforcement Learning” at the IFIP TC7 Conference on System Modelling and Optimization.
(2021/07) Our preprint “Reconstruction of the Density Power Spectrum from Quasar Spectra using Machine Learning” is available on arXiv.
(2021/07) Our paper “Digital Twin Concepts with Uncertainty for Nuclear Power Applications” is published in Energies (special issue Expanding Nuclear Applications and Technologies for a Clean Energy Future).
(2021/07) Our paper “Automated Loss-of-Balance Event Identification in Older Adults at Risk of Falls during Real-World Walking Using Wearable Inertial Measurement Units” is published in Sensors (special issue Wearable Sensors for Gait and Falls Monitoring).
(2021/07) USNCCM
- Jeremiah Hauth gave a presentation “Variational Bayesian Inference for Convolutional Neural Networks in Precision Health Balance Training”.
- Chengyang Huang gave a presentation “Bayesian Inference via Conditional Generative Adversarial Networks”.
- Aniket Jivani gave a presentation “Uncertainty Quantification for Random Field Quantities Using Multifidelity Karhunen-Loeve Expansions”.
- Wanggang Shen gave a presentation “Sequential Optimal Experimental Design Using Reinforcement Learning with Policy Gradient”.
- Xun Huan co-organized a minisymposium “Optimal Experimental Design in Computational Science and Engineering”.
(2021/07) SIAM Annual Meeting
- Xun Huan gave a presentation “Optimal Experimental Design for Variational System Identification of Material Physics Phenomena”.
- Xun Huan co-organized a two-part minisymposium “Physics-Aware Machine Learning for Solving and Discovering PDEs” (parts I, II).
(2021/06) ISBA World Meeting
- Xun Huan gave a presentation “Optimal Bayesian Sequential Design Using Reinforcement Learning with Policy Gradient Methods”.
- Chengyang Huang gave a presentation “Fast Approximate Bayesian Inference via Conditional Generative Adversarial Networks”.
(2021/05) Our paper “Conceptual Design of Extreme Sea-Level Early Warning Systems Based on Uncertainty Quantification and Engineering Optimization Methods” is published in Frontiers in Marine Science.
(2021/05) EMI/PMR Conference
- Xun Huan gave a presentation “Optimal Sequential Bayesian Design of Experiments Using Reinforcement Learning with Policy Gradient”.
- Wanggang Shen gave a presentation “Optimal Bayesian Experimental Design for Variational System Identification of Materials Physics Phenomena”.
(2021/04) Xun Huan gave a presentation “Model-based sequential experimental design” at the USACM TTA Uncertainty Quantification and Probabilistic Modeling Webinar.
(2021/03) Congratulations to Aniket Jivani for successfully passing the Ph.D. Qualifying Exam!
(2021/03) Congratulations to Codie Kawaguchi for winning the NSF Graduate Research Fellowships Program (GRFP) award!
(2021/03) SIAM CSE
- Aniket Jivani gave a presentation “Multifidelity Karhunen-Loeve Expansions for Uncertainty Propagation of Random Field Quantities”.
- Maria Veiga gave a presentation “Including Physical Knowledge into Data Driven Models: Applications in Computational Astrophysics”.
- Saibal De presented a poster “Tensor-Train Decomposition for Data Compression and Data-Driven Reduced Order Modeling”.
- Jeremiah Hauth gave a presentation “Variational Bayesian Inference for Convolutional Neural Networks in Precision Health Balance Training”.
- Xun Huan gave a presentation “Optimal Experimental Design for Variational System Identification of Material Physics Phenomena”.
- Wanggang Shen gave a presentation “Optimal Bayesian Design of Sequential Experiments using Reinforcement Learning with Policy Gradient Methods”.
- Xun Huan co-organized a two-part minisymposium “Model-Based Optimal Experimental Design” (parts I, II).
(2021/02) Our paper “Variational system identification of the partial differential equations governing microstructure evolution in materials: Inference over sparse and spatially unrelated data” is published in Computer Methods in Applied Mechanics and Engineering.
(2021/01) Congratulations to Snehal Prabhudesai for successfully passing the Ph.D. Qualifying Exam!
(2021/01) Aniket Jivani gave a presentation “Uncertainty Quantification for a Turbulent Round Jet Using Multifidelity Karhunen-Loeve Expansions” at the AIAA SciTech Forum. [paper]
(2021/01) WCCM-ECCOMAS Congress
- Wanggang Shen gave a presetation “Sequential Optimal Experimental Design Using Reinforcement Learning with Policy Gradient”.
- Xun Huan co-organized a minisymposium “Optimal Experimental Design in Computational Science and Engineering”.

2020

(2020/12) Xun Huan gave a presentation “Optimal Sequential Bayesian Design of Experiments Using Reinforcement Learning with Policy Gradient” at the Machine Learning in Science & Engineering (MLSE) Conference. [video]
(2020/12) AGU Fall Meeting
- Yulin Pan presented a poster “Optimal Experimental Design of Sensor Locations in EnKF Data Assimilation for Predicting Oceanic Rogue Waves”.
- Gabor Toth gave a presentation “NextGen Space Weather Modeling Framework Using Physics, Data Assimilation, Uncertainty Quantification and GPUs”.
(2020/03) COVID-19 shutdown.
(2020/02) Our paper “Uncertainty Propagation Using Polynomial Chaos Expansions for Extreme Sea-level Hazard Assessment: The Case of the Eastern Adriatic Meteotsunamis” is published in Journal of Physical Oceanography.
(2020/02) Xun Huan gave a presentation “Simulation-based Bayesian Sequential Design Using Reinforcement Learning” at the Michigan State University Department of Statistics and Probability Colloquium.
(2020/01) AIAA SciTech Forum
- Wanggang Shen gave a presentation “Towards Design of Airfoil Pressure Tap Locations for Real-Time Predictions Under Uncertainty Using Bayesian Neural Networks”. [paper]
- Jeremiah Hauth gave a presentation “Correlation Effects in Bayesian Neural Networks for Computational Aeroacoustics Ice Detection”. [paper]
- Beckett Zhou gave a presentation “Correlation Effects in Bayesian Neural Networks for Computational Aeroacoustics Ice Detection”. [paper]

2019

(2019/12) Xun Huan gave a presentation “Simulation-based optimal sequential Bayesian design using policy gradient reinforcement learning” at the International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics).
(2019/11) Our paper “Stochastic surrogate model for meteotsunami early warning system in the easter Adriatic Sea” is published in Journal of Geophysical Research: Oceans.
(2019/11) Xun Huan gave a presentation “Finding the Most Useful Data via Simulation-based Bayesian Experimental Design” at the University of Michigan Integrative Systems + Design (ISD) Manufacturing Seminar Series.
(2019/11) Congratulations to Jeremiah Hauth and Wanggang Shen for successfully passing the Ph.D. Qualifying Exams!
(2019/09) Our paper “Design optimization of a scramjet under uncertainty using probabilistic learning on manifolds” is published in Journal of Computational Physics.
(2019/09) Xun Huan gave a presentation “Optimal Bayesian Design of Sequential Experiments Using Policy Gradient Reinforcement Learning” at the University of Notre Dame Center for Informatics and Computational Science (CICS) Seminar.
(2019/08) Xun Huan gave a presentation “Uncertainty Quantification via Optimal Experimental Design and Bayesian Neural Networks for Aerospace Applications” at the National Institute of Aerospace Computational Fluid Dynamics Seminar.
(2019/08) Xun Huan gave a tutorial lecture “Introduction to Optimal Experimental Design” at the Uncertainty Quantification Summer School. [slides]
(2019/07) USNCCM
- Xun Huan gave a presentation “Linear Experimental Design for Optimal Control Variate Based Surrogate Models”.
- Krishna Garikipati gave a presentation “Variational System Identification of the Partial Differential Equations Governing Pattern-forming Physics: Inference under Varying Fidelity and Noise”.
- Habib Najm gave a presentation “Uncertainty Quantification in Computational Models of Large Scale Physical Systems”.
(2019/07) Xun Huan gave a presentation “Sequential Optimal Experimental Design via Reinforcement Learning” at the Workshop on Machine Learning and Uncertainty Quantification.
(2019/07) Our paper “Variational system identification of the partial differential equations governing the physics of pattern-formation: Inference under varying fidelity and noise” is published in Computer Methods in Applied Mechanics and Engineering.
(2019/07) Xun Huan gave a presentation “Policy Gradient Acceleration for Sequential Bayesian Experimental Design” at the Applied Inverse Problems Conference.
(2019/07) Xun Huan gave a presentation “Optimal Experimental Design and Bayesian Neural Networks for Physics-Based Models” at TU Kaiserslautern Scientific Computing Seminar.
(2019/06) Our paper “Embedded Model Error Representation for Bayesian Model Calibration” is published in International Journal for Uncertainty Quantification.
(2019/06) Beckett Zhou gave a presentation “Towards Real-Time In-Flight Ice Detection Systems via Computational Aeroacoustics and Bayesian Neural Networks” at the AIAA Aviation Forum. [paper]
(2019/05) Wanggang Shen gave a presentation “Optimal Bayesian design of sequential experiments via reinforcement learning” at the International Conference on Design of Experiments (ICODOE).
(2019/04) Our paper “Entropy-based closure for probabilistic learning on manifolds” has been published in Journal of Computational Physics.
(2019/03) Xun Huan gave a presentation “Finding the most useful data via simulation-based Bayesian experimental design” at the University of Michigan Applied and Interdisciplinary Mathematics (AIM) Seminar.
(2019/03) SIAM CSE
- Xun Huan gave a presentation “Global Sensitivity Analysis for Random Fields in Large-Eddy Simulations of Scramjet Computations”.
- Khachik Sargsyan gave a presentation “Bayesian Inference for Structural Error Quantification”.
- Andrew Davis gave a presentation “Selecting Multiple Borehole Locations for Maximizing Bayesian Information Gain on Groundwater Transmissivity”.
- Xun Huan co-organized a two-part minisymposium “Optimal Experimental Design for Inverse Problems” (parts I, II).
(2019/01) Our paper “Compressive sensing adaptation for polynomial chaos expansions” has been published in Journal of Computational Physics.
(2019/01) AIAA SciTech Forum
- Xun Huan gave a presentation “Uncertainty Propagation Using Conditional Random Fields in Large-Eddy Simulations of Scramjet Computations”. [paper]
- Gianluca Geraci gave a presentation “Progress in Scramjet Design Optimization Under Uncertainty Using Simulations of the HIFiRE Direct Connect Rig”. [paper]

2018

(2018/12) Our paper “Enhancing Model Predictability for a Scramjet Using Probabilistic Learning on Manifolds” has been published in AIAA Journal.
(2018/12) Andrew Davis gave a presentation “Selecting Multiple Borehole Locations for Maximizing Bayesian Information Gain on Past Ice Sheet Surface Temperatures” at the AGU Fall Meeting.
(2018/11) Xun Huan gave a presentation “Finding the Most Informative Data Using Model-based Bayesian Experimental Design” at the Oakland University Department of Physics Colloquium.
(2018/10) Our paper “Uncertainty Assessment of Octane Index Framework for Stoichiometric Knock Limits of Co-Optima Gasoline Fuel Blends” has been published in SAE International Journal of Fuels and Lubricants.
(2018/08) Jeremiah Hauth and Wanggang Shen joined the group!
(2018/06) Our paper “Compressive sensing with Cross-Validation and Stop-Sampling for Sparse Polynomial Chaos Expansions” has been published in SIAM/ASA Journal on Uncertainty Quantification.
(2018/03) Our paper “Global Sensitivity Analysis and Estimation of Model Error, Toward Uncertainty Quantification in Scamjet Computations” has been published in AIAA Journal.
(2018/08) Xun Huan moved to Ann Arbor, MI to start a new position as an Assistant Professor of Mechanical Engineering at the University of Michigan.
(2018/07) Joint Statistical Meetings
- Xun Huan gave a speed presentation “Simulation-Based Bayesian Optimal Design for Ice Sheet Borehole Experiments” (oral and poster parts).
- Khachik Sargsyan gave a presentation “Bayesian Framework for Embedded Model Error Representation and Quantification”.
(2018/07) SIAM Annual Meeting
- Xun Huan gave a presentation “Optimal Bayesian Experimental Design of Borehole Locations for Inferring Past Ice Sheet Surface Temperature”.
- Cosmin Safta gave a presentation “Adaptive Sparse Quadrature for Multifidelity Scramjet Flow Simulations”.
- Khachik Sargsyan gave a presentation “Bayesian Inference for Model Error Quantification and Propagation with UQTk”.
(2018/06) Xun Huan presented a poster “Choosing Embedding for Capturing Model Misspecification Using Global Sensitivity Analysis and Bayes Factor Computation” at the ISBA World Meeting.
(2018/06) Xun Huan gave a presentation “Simulation-based Bayesian Experimental Design for Computationally Intensive Models” at the University of Cambridge Isaac Newton Institute for Mathematical Sciences, Programme on Uncertainty Quantification for Complex Systems: Theory and Methodologies, and as a part of the Manchester-Southampton-Glasgow Design of Experiments Seminar Series.
(2018/06) Xun Huan gave a presentation “Value of Feedback and Lookahead in Bayesian Sequential Optimal Experimental Design” at the Joint Research Conference on Statistics in Quality, Industry, and Technology.
(2018/04) SIAM UQ
- Xun Huan gave a presentation “Compressive Sensing with Cross-Validation and Stop-Sampling for Sparse Polynomial Chaos Expansions”.
- Xun Huan co-organized a three-part minisymposium “Model-Based Optimal Experimental Design” (parts I, II, III).
(2018/02) Xun Huan gave a presentation “Optimal Sequential Bayesian Experimental Design” at the workshop “Foresight for Making Good Future Predictions—Lookahead Optimization in Artificial and Natural Systems” held by the Santa Fe Institute.
(2018/01) Xun Huan gave a presentation “Multifidelity Statistical Analysis of Large Eddy Simulations in Scramjet Computations” at the AIAA SciTech Forum. [paper]

2017

(2017/12) Andrew Davis gave a presentation “Optimal Experimental Design of Borehole Locations for Bayesian Inference of Past Ice Sheet Surface Temperatures” at the AGU Fall Meeting.