Extracting Interpretable Physical Parameters from Spatiotemporal Systems
using Unsupervised Learning

Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning

13 July 2019

Papers citing "Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning"

10 / 10 papers shown

Title
Learning Attentive Neural Processes for Planning with Pushing Actions Atharv Jain Seiji Shaw Nicholas Roy 110 0 0 24 Apr 2025
From Spectra to Biophysical Insights: End-to-End Learning with a Biased Radiative Transfer Model Yihang She Clement Atzberger Andrew Blake Srinivasan Keshav 16 0 0 05 Mar 2024
An analysis of Universal Differential Equations for data-driven discovery of Ordinary Differential Equations Mattia Silvestri Federico Baldo Eleonora Misino M. Lombardi AI4CE 16 1 0 17 Jun 2023
Neuro-symbolic partial differential equation solver Pouria A. Mistani Samira Pakravan Rajesh Ilango S. Choudhry Frédéric Gibou 26 1 0 25 Oct 2022
JAX-DIPS: Neural bootstrapping of finite discretization methods and application to elliptic problems with discontinuities Pouria A. Mistani Samira Pakravan Rajesh Ilango Frédéric Gibou 16 8 0 25 Oct 2022
GD-VAEs: Geometric Dynamic Variational Autoencoders for Learning Nonlinear Dynamics and Dimension Reductions Ryan Lopez P. Atzberger AI4CE 24 7 0 10 Jun 2022
Surrogate- and invariance-boosted contrastive learning for data-scarce applications in science Charlotte Loh T. Christensen Rumen Dangovski Samuel Kim Marin Soljacic 24 16 0 15 Oct 2021
Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure Samuel Kim Peter Y. Lu Charlotte Loh Jamie Smith Jasper Snoek M. Soljavcić BDL AI4CE 63 17 0 23 Apr 2021
Differentiable Molecular Simulations for Control and Learning Wujie Wang Simon Axelrod Rafael Gómez-Bombarelli AI4CE 100 49 0 27 Feb 2020
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics C. Wehmeyer Frank Noé AI4CE BDL 109 355 0 30 Oct 2017