v1v2v3v4 (latest)

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"

25 / 25 papers shown

Title
Learning to Decouple Complex Systems Zihan Zhou Tianshu Yu BDL 148 4 0 17 Feb 2025
DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Yaohua Zang P. Koutsourelakis AI4CE 107 1 0 10 Feb 2025
A Primer on Variational Inference for Physics-Informed Deep Generative Modelling Alex Glyn-Davies Arnaud Vadeboncoeur O. Deniz Akyildiz Ieva Kazlauskaite Mark Girolami PINN 112 0 0 10 Sep 2024
Closure Discovery for Coarse-Grained Partial Differential Equations Using Grid-based Reinforcement Learning Jan-Philipp von Bassewitz Sebastian Kaltenbach Petros Koumoutsakos AI4CE 114 2 0 01 Feb 2024
GP-VAE: Deep Probabilistic Time Series Imputation Vincent Fortuin Dmitry Baranchuk Gunnar Rätsch Stephan Mandt BDL AI4TS 72 257 0 09 Jul 2019
Physics-Informed Probabilistic Learning of Linear Embeddings of Non-linear Dynamics With Guaranteed Stability Shaowu Pan Karthik Duraisamy 95 138 0 09 Jun 2019
Learning interpretable continuous-time models of latent stochastic dynamical systems Lea Duncker G. Bohner Julien Boussard M. Sahani 46 77 0 12 Feb 2019
A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the Small Data regime Constantin Grigo P. Koutsourelakis AI4CE 108 26 0 11 Feb 2019
Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems Yibo Yang P. Perdikaris SyDa BDL AI4CE 56 56 0 15 Jan 2019
Deep Generative Markov State Models Hao Wu Andreas Mardt Luca Pasquali Frank Noe AI4CE 38 60 0 19 May 2018
Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks P. Stinis Tobias J. Hagge A. Tartakovsky Enoch Yeung GAN AI4CE 68 32 0 22 Mar 2018
Physics-constrained, data-driven discovery of coarse-grained dynamics L. Felsberger P. Koutsourelakis AI4CE 47 19 0 11 Feb 2018
Semi-Amortized Variational Autoencoders Yoon Kim Sam Wiseman Andrew C. Miller David Sontag Alexander M. Rush BDL DRL 148 243 0 07 Feb 2018
Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations M. Raissi P. Perdikaris George Karniadakis PINN AI4CE 96 615 0 28 Nov 2017
Bayesian model and dimension reduction for uncertainty propagation: applications in random media Constantin Grigo P. Koutsourelakis 66 31 0 07 Nov 2017
VAMPnets: Deep learning of molecular kinetics Andreas Mardt Luca Pasquali Hao Wu Frank Noé 68 546 0 16 Oct 2017
Variational approach for learning Markov processes from time series data Hao Wu Frank Noé BDL AI4TS 37 265 0 14 Jul 2017
A State Space Approach for Piecewise-Linear Recurrent Neural Networks for Reconstructing Nonlinear Dynamics from Neural Measurements Daniel Durstewitz 225 56 0 23 Dec 2016
Structured Inference Networks for Nonlinear State Space Models Rahul G. Krishnan Uri Shalit David Sontag BDL 93 459 0 30 Sep 2016
Predictive Coarse-Graining M. Schöberl N. Zabaras P. Koutsourelakis 143 34 0 26 May 2016
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems Martín Abadi Ashish Agarwal P. Barham E. Brevdo Zhiwen Chen ... Pete Warden Martin Wattenberg Martin Wicke Yuan Yu Xiaoqiang Zheng 289 11,150 0 14 Mar 2016
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 2.1K 150,364 0 22 Dec 2014
Auto-Encoding Variational Bayes Diederik P. Kingma Max Welling BDL 458 16,922 0 20 Dec 2013
Variational Relevance Vector Machines Charles M. Bishop Michael E. Tipping DRL 83 428 0 16 Jan 2013
Stochastic Variational Inference Matt Hoffman David M. Blei Chong-Jun Wang John Paisley BDL 265 2,628 0 29 Jun 2012