St. Mark's School of Texas

most citedGradient-enhanced physics-informed neural networks for forward and inverse PDE problems

714 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

Dylan J. Altschuler1 · h 5
G. Karniadakis1 · h 144
J. Yu1 · h 1
Lani F. Wu1 · h 36
Lu Lu1 · h 26
S. Altschuler1 · h 42
Sigurd Angenent1 · h 3
Xuhui Meng1 · h 23

collaborating institutions

Brown UniversityUS1 paper
The University of Texas Southwestern Medical CenterUS1 paper
University of PennsylvaniaUS1 paper
University of Wisconsin–MadisonUS1 paper

2 papers

cs.LG2021★ 714 cited

Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems

Jeremy Yu, Lu Lu, Xuhui Meng +1

Deep learning has been shown to be an effective tool in solving partial differential equations (PDEs) through physics-informed neural networks (PINNs). PINNs embed the PDE residual…

math.DG2012★ 19 cited

The Zoo of Solitons for Curve Shortening in $R^{n}$

Dylan J. Altschuler, Steven J. Altschuler, Sigurd B. Angenent +1

We provide a detailed description of solutions of Curve Shortening in $R^{n}$ that are invariant under some one-parameter symmetry group of the equation, paying particular attention…

Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems

The Zoo of Solitons for Curve Shortening in Rn

The Zoo of Solitons for Curve Shortening in $R^{n}$