Solving parametric high-Reynolds-number wall-bounded turbulence around airfoils governed by Reynolds-averaged Navier–Stokes equations using time-stepping …
Physics-informed neural networks (PINNs) have recently emerged as popular methods for
solving forward and inverse problems governed by partial differential equations. However …
solving forward and inverse problems governed by partial differential equations. However …
Deep adaptive sampling for surrogate modeling without labeled data
Surrogate modeling is of great practical significance for parametric differential equation
systems. In contrast to classical numerical methods, using physics-informed deep learning …
systems. In contrast to classical numerical methods, using physics-informed deep learning …
An Adjoint-Oriented Meta-Auto-Decode Method for Solving Parameterized Optimal Control Problems
J Yong, X Luo, S Sun, C Ye - Available at SSRN 4951506 - papers.ssrn.com
Abstract A novel Adjoint-Oriented Meta-Auto-Decoder (AOMAD) method is proposed to
solve parameterized optimal control problems. This method adopts a pre-training-fine-tuning …
solve parameterized optimal control problems. This method adopts a pre-training-fine-tuning …