245 citations · 783 across the 82 of their papers we have counts for
21 papers · 1 filter
Neural Operator Splitting for Fluid Dynamics in Complex Geometry
Jianxiang Li, Xi Deng, Hong Chul Nam +1
Solving dynamical systems in complex and diverse geometries remains challenging for both numerical solvers and learning-based surrogate models. We propose Neural Operator Splitting…
Learnable Splines for Unstructured Neural Operators
Vignesh Gopakumar, Zhuoyuan Wang, Ander Gray +12
Neural operators approximate solution operators of partial differential equations at a fraction of the cost of numerical solvers. Efficient grid-based architectures such as the Fou…
Beyond DFT electronic band structures via dual-space orbital learning
Minhyuk Kang, Beom Seok Kang, Nikolai Argatoff +4
Accurate electronic band structures are essential for predicting material properties, but high-fidelity calculations are computationally demanding. We introduce O-kRystal, a dual-s…
Physics is the Best Teacher: Consistency Learning for Time-Invariant Operators of Chaotic Dynamics
Lufang Chiang, Jiachen Yao, Thomas Y. L. Lin +1
Accelerating the prediction of long-term behavior in chaotic systems is crucial in scientific computing. However, existing methods rely on numerical solvers or autoregressive model…
Counterfactual Predictions in Scientific Emulators Without Controlled Experiments
Dingling Yao, Kahaan Gandhi, Valentin Duruisseaux +3
Many scientific questions require reasoning about what was never observed: What if the conditions, interventions, or history had been different? Models can predict accurately on ob…
PTNO: Training Neural Operators with Noisy Monte Carlo Estimates for Particle Transport Problems
Yubo Cao, Xi Deng, Mengqi Xia +3
Particle transport under multiple scattering is central to radiative transfer and plasma physics, yet high-fidelity Monte Carlo (MC) simulations must trace prohibitively many parti…