Accelerating the prediction of long-term behavior in chaotic systems is crucial in scientific computing. However, existing methods rely on numerical solvers or autoregressive models that advance one small step at a time, which makes long horizons expensive. We instead view this problem as learning the system's time-inv...
Lu-Fang Chiang, Jia-Chen Yao, Thomas Y. L. Lin et al.· 0 citations
Previous work built AI-based surrogates for a nonlinear gyrokinetic simulation code with the goal of using them for fast, direct calculations of turbulent ion heat flux in stellarator design optimizations and scenario planning for experiments. These AI surrogates were trained on data from>200k nonlinear, adiabatic elec...
R. Churchill, M. Landreman, Jong Youl Choi et al.· 0 citations
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 observed data yet fail on such what-if queries when correlated inputs are varied independently. A common remedy is to add controlled simulatio...
Ding-Ling Yao, Kahaan Gandhi, Valentin Duruisseaux et al.· 0 citations
The two-dimensional sphere embedded in three-dimensional Euclidean space S2, plays a central role in a variety of scientific and engineering domains, including geophysics, planetary science, geodesy, atmospheric physics, quantum chemistry, cosmology, and virtual reality, among many others. As machine learning increasin...
Thorsten Kurth, M. Rietmann, M. Bisson et al.· 0 citations
Particle transport under multiple scattering is central to radiative transfer and plasma physics, yet high-fidelity Monte Carlo (MC) simulations must trace prohibitively many particles. Learning-based surrogates can amortize this cost, but typically train on expensive, well-converged MC solutions. We propose the Partic...
Yu-Bo Cao, Xi Deng, Meng-Qi Xia et al.· 0 citations
In urban low-altitude flight, buildings reshape ambient wind into spatially varying 3D flow, making unmanned aerial vehicle (UAV) energy depend on local wind exposure as well as path length. However, building-resolved wind information is rarely available when a mission must be planned. Computational fluid dynamics (CFD...
Shao-Xiang Qin, Yu-Cheng Zhao, Fuyuan Lyu et al.· 0 citations
Generative models are increasingly used to solve scientific inverse problems, but existing evaluations still focus primarily on whether a method can produce a single plausible reconstruction. This is insufficient for ill-posed problems, where multiple solutions may be consistent with the same sparse or noisy observatio...
Jia-Chen Yao, Zi-Siang Hsu, Xi Deng et al.· 0 citations
This work proposes Seq2Contact, a novel method to predict the protein-NA binding at a single nucleotide (DNA) and single amino acid (Protein) level, which is computationally more efficient, with up to 80% less memory cost and more than 90% less inference time.
D. Rajwade, Erica Wang, Aryan Satpathy et al.· 1 citation
Equation recast is introduced, which reformulates parametric operator learning as the learning of a single canonical operator, and provides a route toward reusable neural PDE solvers combining equation-guided transfer, data efficiency, and monitorable inference.
Qi-Yun Cheng, Valentin Duruisseaux, C. Clauser et al.· 0 citations
EquiReg formalizes manifold-preferential equivariant functions that exhibit low equivariance error for on-manifold samples and high error for off-manifold ones, thereby guiding sampling toward symmetry-preserving regions of the solution space.
Method is introduced, which augments SOAP-style preconditioning with a scalar secant-energy correction adapted to Kronecker geometry and an adaptive basis update followed by variance-state downscaling, and is positioned as a scalable option for stiff, high-accuracy physics-informed training, rather than a uniform repla...
Guang-Yuan Wang, Mads Toftrup, Sebastian Loeschcke et al.· 0 citations
The results show that operator-learning surrogates can enable inverse design in quantum systems whose Hilbert spaces are too large for conventional direct optimization and enable inverse design in quantum systems whose Hilbert spaces are too large for conventional direct optimization.
A. Pipi, Valentin Duruisseaux, Emily M. Been et al.· 0 citations
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