Poster Presenters
Poster presenters
Deepak Akhare, University of Notre Dame
Neural Differentiable Model for Fiber-Reinforced Composites Manufacturing
POSTER
Mauricio Aristizabal, The University of Texas at San Antonio
Uncertainty Quantification of Finite Element Models Using High-Order Derivatives Computed via Hypercomplex Numbers
POSTER
Enrico Camporeale, University of Colorado
ACCRUE: Accruate and Reliable Uncertainty Estimate in Deterministic Models
POSTER
Joseph Choi, University of Virginia
Physics-Aware AI-Directed Framework for Microstructural Design of Shocked Materials
Saibal De, Sandia National Laboratories
Uncertainty Propagation in Dynamical Systems via Stochastic Collocation on Model Dynamics
POSTER
Danial Faghihi, University at Buffalo
An Adaptive Selection of Predictive Computational Models
POSTER
Georgios Georgalis, Tufts University
Data and Model Uncertainty in Deep CNNs: An Application to U-Net with Image Data from Combustion Experiments
POSTER
Kundan Goswami, Protection Engineering Consultants, LLC
Efficient Estimation of Reinforced Concrete Wall Impact Damage Probability Using Physics-Guided Convolutional Neural Netwark Based Surrogate Model
POSTER
Ashwini Gupta, Johns Hopkins University
Modeling Composites at Multiscale and Studying Microstructure Induced Uncertainty Using a Deep Learning Approach
POSTER
Joseph Hart, Sandia National Laboratories
Characterizing and Propagating Model Discrepancy in PDE-Constrained Optimization
POSTER
Mark Hobbs, University of Exeter / The Alan Turing Institute
A Multilevel Monte-Carlo Framework for Peridynamic Models
Shenglin Huang, University of Pennsylvania
Variational Onsager Neural Networks (VONNs): A Thermodynamics-Based Variational Learning Strategy for Non-Equilibrium Material
POSTER
Teeratorn Kadeethum, Sandia National Laboratories
Barlow Twins Reduced Order Modeling with Uncertainty Quantification for Contact Problems
POSTER
Dongjin Lee, University of California San Diego
A Bi-Fidelity Method for Coherent Risk Assessment of Non-Linear Systems Under Dependent and High-Dimensional Random Variables
POSTER
William E. Lewis, Sandia National Laboratories
Multi-Task Machine Learning for Fusion Simulations
Xin-Yang Liu, University of Notre Dame
Off-Policy Reinforcement Learning for Finsh-Fin-Ray Control Trained in an Asynchronous Parallel Manner
Simon Mak, Duke University
Physics-Integrated Gaussian Process Learning for Cost-Efficient Control Training of Diesel Engines
Rambod Mojgani, Rice University
Discovery of Interpretable Structural Model Errors by Combining Bayesian Sparse Regression and Data Assimilation
POSTER
Hai Van Nguyen, The University of Texas at Austin
TNET: Model-Constrained Deep Learning Approach for Inverse Problems
POSTER
Antonin Paquette-Rufiange, Polytechnique Montréal
Optimal Design of Validation Experiments Using Sensitivity Indices
POSTER
Randy Price, George Mason University
NINNs: Nudging Induced Neural Networks
Teo Price-Broncucia, University of Colorado - Boulder
Multi-Time Unscented Kalman Inversion for Calibration of Expensive Chaotic Models
POSTER
Serge Prudhomme, Polytechnique Montreal
Approximating the Operator of the Wave Equation Using Deep Learning
POSTER
Khachik Sargsyan, Sandia National Laboratories
Uncertainty Quantification of Machine Learning Interatomic Potential Models
POSTER
Daniel Seidl, Sandia National Laboratories
Interlaced Characterization and Calibration of Elastoplastic Constitutive Models
POSTER
Harsh Sharma, University of California San Diego
Bayesian Identification of Nonseparable Hamiltonian Systems Using Stochastic Dynamic Models
POSTER
Nolan Strauss, University of Utah
Application of a Bayesian Framework for Plasticity Model Selection
Hamid Taghavi Ganji, University of Nevada Reno
Multi-Fidelity Modeling for Uncertainty Quantification in Earthquake Engineering Applications
POSTER
Ponkrshnan Thiagarajan, Michigan Technological University
Uncertainty Quantification for Bayesian Convolutional Neural Network Based Surrogate Models
POSTER
Marten Thompson, University of Minnesota
Location-Aware Discrimination in Physics Informed Generative Adversarial Networks
POSTER
Anh Tran, Sandia National Laboratories
Microstructure-Sensitive Uncertainty Quantification for Materials Constitutive Models in Crystal Plasticity Finite Element Method
POSTER
Ruben Villarreal, Sandia National Laboratories
Reinforcement Learning for Material Calibration Via Kalman Filter Estimation
POSTER
Jinming Wan, State University of New York at Binghamton
Uncertainty Quantification and Optimal Robust Design for Machining Operations
POSTER
Ting Wang, DEVCOM Army Research Laboratory
Deep Learning Enhanced Uncertainty Quantification
Ruda Zhang, Duke University
Gaussian Process Subspace Regression for Parametric Studies of Structural Systems