Space and Time Continuous Physics Simulation From Partial Observations

arXiv:2401.09198v2 Announce Type: replace
Abstract: Modern techniques for physical simulations rely on numerical schemes and mesh-refinement methods to address trade-offs between precision and complexity, but these handcrafted solutions are tedious and require high computational power. Data-driven methods based on large-scale machine learning promise high adaptivity by integrating long-range dependencies more directly and efficiently. In this work, we focus on fluid dynamics and address the shortcomings of a large part of the literature, which are based on fixed support for computations …

abstract arxiv complexity computational continuous cs.lg data data-driven dependencies machine machine learning mesh modern numerical physics power precision scale simulation simulations solutions space space and time trade type