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