arXiv:2606.15053v1 Announce Type: new Abstract: Surrogate models are central to scientific machine learning, where they enable fast prediction, simulation, inference, and control for complex physical systems. For time-dependent problems, however, accurate interpolation of training trajectories is not sufficient: reliable surrogates should also respect the conservation laws, invariants, admissibility conditions, and dissipative structures that give those trajectories physical meaning. We introduce Physics-conforming Latent Twins, a framework for learning latent surrogate solution operators whos
The increasing complexity of physical systems and the drive for more energy-efficient and reliable AI models necessitate new approaches to surrogate modeling that incorporate foundational physics.
Reliable physics-conforming AI models are critical for accelerating scientific discovery, optimizing industrial processes, and developing robust AI systems for real-world applications where physical laws are paramount.
This framework significantly enhances the accuracy and trustworthiness of AI surrogates for time-dependent physical systems, moving beyond simple data interpolation to incorporate fundamental physical principles.
- · Scientific machine learning researchers
- · Engineering sectors (aerospace, automotive, energy)
- · AI model developers
- · High-performance computing (HPC) providers
- · Developers of purely data-driven surrogate models
- · Sectors reliant on slow, traditional simulation methods
More accurate and faster simulations for complex physical problems become broadly accessible through AI.
This could lead to accelerated design cycles for new materials, drugs, and industrial processes, reducing development costs and time-to-market.
The democratization of physics-informed AI might elevate countries with strong fundamental science and engineering capabilities into new strategic positions in advanced technology development.
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Read at arXiv cs.LG