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05/17/2021
Trust Region Method for Coupled Systems of PDE Solvers and Deep Neural Networks
Physics-informed machine learning and inverse modeling require the solut...
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11/24/2020
ADCME: Learning Spatially-varying Physical Fields using Deep Neural Networks
ADCME is a novel computational framework to solve inverse problems invol...
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11/02/2020
Distributed Machine Learning for Computational Engineering using MPI
We propose a framework for training neural networks that are coupled wit...
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08/30/2020
Solving Inverse Problems in Steady State Navier-Stokes Equations using Deep Neural Networks
Inverse problems in fluid dynamics are ubiquitous in science and enginee...
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05/09/2020
Inverse Modeling of Viscoelasticity Materials using Physics Constrained Learning
We propose a novel approach to model viscoelasticity materials using neu...
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04/01/2020
Learning Constitutive Relations using Symmetric Positive Definite Neural Networks
We present the Cholesky-factored symmetric positive definite neural netw...
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02/24/2020
Physics Constrained Learning for Data-driven Inverse Modeling from Sparse Observations
Deep neural networks (DNN) have been used to model nonlinear relations b...
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12/16/2019
Learning Hidden Dynamics using Intelligent Automatic Differentiation
Many engineering problems involve learning hidden dynamics from indirect...
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10/15/2019
Adversarial Numerical Analysis for Inverse Problems
Many scientific and engineering applications are formulated as inverse p...
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12/20/2018
