arXiv:2605.28317v1 Announce Type: new Abstract: Neural surrogates promise large speedups over classical solvers for physical dynamics but fail silently at sharp dynamical events such as shocks, fronts, and contact. We present hybrid neural world models for physical dynamics: a recipe for training and deploying multi-horizon surrogates in physical state space, where a single network with continuous horizon conditioning is trained with direct supervision against textbook reference solvers to predict any future state at horizon T in one forward pass. Although no part of the training data, loss fu
The continuous development in AI and machine learning techniques, particularly in areas like neural networks and surrogate modeling, enables increasingly sophisticated approaches to simulating complex physical systems.
Improving the accuracy and reliability of AI surrogates for physical dynamics, especially at critical junctures like shocks and fronts, is crucial for scientific discovery, engineering design, and operational efficiency across many high-stakes domains.
The ability to integrate AI models with robust, classical solvers for physical dynamics fundamentally changes the paradigm for simulating and predicting physical phenomena, making AI more trustworthy in critical applications.
- · Scientific R&D
- · Engineering industries
- · High-performance computing
- · AI/ML researchers
- · Traditional physical simulation software (if not integrated with AI)
- · Organizations reliant solely on classical solvers for complex dynamics
Faster and more accurate simulations of complex physical systems become commonplace, accelerating research and development.
New designs and materials are discovered more rapidly, leading to breakthroughs in fields like aerospace, energy, and microelectronics.
The reduced computational cost and time for physical simulations could democratize access to advanced engineering capabilities, fostering innovation globally.
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Read at arXiv cs.LG