arXiv:2601.22123v4 Announce Type: replace Abstract: Simulating the long-time evolution of Hamiltonian systems is limited by the small timesteps required for stable numerical integration. To overcome this constraint, we introduce a framework to learn Hamiltonian Flow Maps by predicting the mean phase-space evolution over a chosen time span, enabling stable large-timestep updates far beyond the stability limits of classical integrators. To this end, we impose a Mean Flow consistency condition for time-averaged Hamiltonian dynamics. Unlike prior approaches, this allows training on independent pha
This research addresses a fundamental limitation in simulating complex physical systems, which is increasingly critical as AI models are applied to scientific and engineering problems.
Improving the efficiency and stability of molecular dynamics simulations could accelerate discoveries in materials science, drug discovery, and other fields reliant on understanding atomic-level interactions.
The ability to perform stable large-timestep simulations will enable researchers to model longer biological processes or material phenomena with greater accuracy and less computational overhead.
- · Pharmaceutical companies
- · Materials science researchers
- · AI compute infrastructure providers
- · Drug discovery platforms
- · Companies reliant on conventional simulation methods
More efficient and accurate simulation of complex molecular and material systems becomes possible.
Accelerated development cycles for new drugs, chemicals, and advanced materials due to faster computational modeling.
Enhanced AI systems for scientific discovery that can leverage more extensive and reliable simulation data, leading to breakthroughs in areas like sustainable energy or advanced manufacturing.
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Read at arXiv cs.LG