A Comparison of Mesh-Free Differentiable Programming and Data-Driven Strategies for Optimal Control under PDE Constraints

2 October 2023

Papers citing "A Comparison of Mesh-Free Differentiable Programming and Data-Driven Strategies for Optimal Control under PDE Constraints"

6 / 6 papers shown

Title
JAX-FLUIDS: A fully-differentiable high-order computational fluid dynamics solver for compressible two-phase flows Deniz A. Bezgin Aaron B. Buhendwa Nikolaus A. Adams AI4CE 65 71 0 25 Mar 2022
Learning to Control PDEs with Differentiable Physics Philipp Holl V. Koltun Nils Thuerey AI4CE PINN 79 189 0 21 Jan 2020
JAX, M.D.: A Framework for Differentiable Physics S. Schoenholz E. D. Cubuk 50 38 0 09 Dec 2019
Hidden Fluid Mechanics: A Navier-Stokes Informed Deep Learning Framework for Assimilating Flow Visualization Data M. Raissi A. Yazdani George Karniadakis AI4CE PINN 93 161 0 13 Aug 2018
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 886 9,364 0 06 Jun 2015
Automatic differentiation in machine learning: a survey A. G. Baydin Barak A. Pearlmutter Alexey Radul J. Siskind PINN AI4CE ODL 174 2,824 0 20 Feb 2015