Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems

30 December 2019

Papers citing "Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems"

8 / 8 papers shown

Title
Learning to Decouple Complex Systems Zihan Zhou Tianshu Yu BDL 66 4 0 17 Feb 2025
DGNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Yaohua Zang P. Koutsourelakis AI4CE 52 0 0 10 Feb 2025
A Primer on Variational Inference for Physics-Informed Deep Generative Modelling Alex Glyn-Davies A. Vadeboncoeur O. Deniz Akyildiz Ieva Kazlauskaite Mark Girolami PINN 58 0 0 10 Sep 2024
Physics-Aware Neural Implicit Solvers for multiscale, parametric PDEs with applications in heterogeneous media Matthaios Chatzopoulos P. Koutsourelakis AI4CE 29 3 0 29 May 2024
Closure Discovery for Coarse-Grained Partial Differential Equations Using Grid-based Reinforcement Learning Jan-Philipp von Bassewitz Sebastian Kaltenbach P. Koumoutsakos AI4CE 30 1 0 01 Feb 2024
Predicting and explaining nonlinear material response using deep Physically Guided Neural Networks with Internal Variables Javier Orera-Echeverria J. Ayensa-Jiménez Manuel Doblaré 18 1 0 07 Aug 2023
Physics-enhanced Neural Networks in the Small Data Regime Jonas Eichelsdörfer Sebastian Kaltenbach P. Koutsourelakis AI4CE PINN 6 5 0 19 Nov 2021
Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling Naoya Takeishi Alexandros Kalousis DRL AI4CE 22 54 0 25 Feb 2021