Xuan Zhang

PhD student @TAMU. Working on AI for science. Previously @sjtu1896 and @ENSTAParis.

[Xuan Zhang @floatlazer]

Excited to share that our paper "Orbital Transformers for Predicting Wavefunctions in Time-Dependent Density Functional Theory" is accepted to #ICLR26! Huge thanks to collaborators, advisors, reviewers, ACs, PCs and organizers!

[Xuan Zhang @floatlazer]

https://t.co/AO0y60jwE7

[Xuan Zhang @floatlazer]

Excited to share that our paper "Orbital Transformers for Predicting Wavefunctions in Time-Dependent Density Functional Theory" is accepted to #ICLR26! Huge thanks to collaborators, advisors, reviewers, ACs, PCs and organizers!

[Shuiwang Ji @ShuiwangJi]

Our 500+ page AI4Science paper is finally published:

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. Foundations and Trends® in Machine Learning, Vol. 18, No. 4, 385–912, 2025

https://t.co/RzxYTDOJwx

[Jacob Helwig @JacobHelwig]

(2/2) I'll be presenting GeoMPNN at the competition workshop this Saturday, 12/13, at 3:40 PM PST in West Meeting Room 208.

GeoMPNN Team: @floatlazer @haiyangyu14 @ShuiwangJi

[Jacob Helwig @JacobHelwig]

We recently developed GeoMPNN (https://t.co/ClsKI6sBb6), a machine-learned simulator for airfoil flows. GeoMPNN is designed to generalize across different airfoil shapes and flow conditions and won best student solution at the NeurIPS 2024 ML4CFD Competition. (1/2)

[Xuan Zhang @floatlazer]

Paper link: https://t.co/owOIuZ7mlp
And more time zones: 15:00 - 16:15 (CEST) / 09:00 - 10:15 (EDT) / 08:00 - 09:15 (CDT)

[Xuan Zhang @floatlazer]

Jacob @JacobHelwig and I will present our paper "SineNet: Learning Temporal Dynamics in Time-Dependent Partial Differential Equations" tomorrow (May 13) from 09:00 to 10:15 EDT in the AI4ScienceTalk series @AI4scienceTalks. Come and join us!.
Details: https://t.co/WBx6SgsDN6

[Xuan Zhang @floatlazer]

Come chat with Jacob and me about SineNet and neural PDE solvers in Friday morning’s poster session!

[Jacob Helwig @JacobHelwig]

Looking forward to presenting our work on temporal dynamics modeling at #ICLR2024! We will be poster #30 on Friday morning from 10:45 to 12:45PM Vienna time.

[Johannes Brandstetter @jo_brandstetter]

This is a super interesting paper, proving once more how important residual connections are. In this case residuals are interpreted as small transformations over time, strongly improving the UNet idea for spatio-temporal problems! I learned a lot when reading this :)

[Xuan Zhang @floatlazer]

Check out our #ICLR2024 paper, “SineNet: Learning Temporal Dynamics in Time-Dependent Partial Differential Equations” (https://t.co/mVEUeRiOyu).
U-Nets are widely used for static image segmentation tasks in computer vision. [1/N]

[Xuan Zhang @floatlazer]

Shout out to the co-first author @JacobHelwig, my advisor @ShuiwangJi and all the collaborators. [8/N, N=8]

[Jacob Helwig @JacobHelwig]

Check out our #ICLR2024 paper, “SineNet: Learning Temporal Dynamics in Time-Dependent Partial Differential Equations” (https://t.co/bgismSzI7W), where we improve the U-Net architecture for modeling time-evolving PDEs. (1/4)

[Maurice Weiler @maurice_weiler]

We proudly present our 524 page book on equivariant convolutional networks.
Coauthored by Patrick Forré, @erikverlinde and @wellingmax.
https://t.co/y9YBpqhyLG
[1/N]