arXiv:2505.18647v3 Announce Type: replace Abstract: Simulating trajectories of dynamical systems is a fundamental problem in a wide range of fields such as molecular dynamics, biochemistry, and pedestrian dynamics. Machine learning has become an invaluable tool for scaling physics-based simulators and developing models directly from experimental data. In particular, recent advances in deep generative modeling and geometric deep learning enable probabilistic simulation by learning complex trajectory distributions while respecting intrinsic permutation and time-shift symmetries. However, traject
The proliferation of deep generative modeling and geometric deep learning coincides with increasing demand for robust and scalable simulation tools across diverse scientific and engineering fields.
This research introduces methods for more accurate and probabilistic simulation of complex dynamical systems, which is critical for scientific discovery and advanced engineering applications.
The ability to simulate trajectories with better data coupling and symmetry preservation enhances the reliability and applicability of machine learning in scientific simulation.
- · AI researchers
- · Bio-tech industry
- · Materials science
- · Robotics
- · Traditional simulation methods
- · Computationally-limited research
Improved predictive models in fields like molecular dynamics and pedestrian flow.
Accelerated drug discovery, materials design, and potentially safer autonomous systems.
New scientific discoveries enabled by the ability to simulate previously intractable complex systems.
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