phi-rom/PhiROM
Repository for NeurIPS 2025 paper, "Physics-informed Reduced Order Modeling of Time-dependent PDEs via Differentiable Solvers."
This project helps engineers and scientists build better predictive models for complex systems described by time-dependent partial differential equations (PDEs), such as fluid dynamics or heat transfer. It takes observational data, even if sparse, from a system and incorporates the underlying physics to produce highly accurate, long-term forecasts of how the system will evolve under different conditions. Users are researchers or practitioners in fields like engineering, physics, or climate science who need to simulate and understand complex physical phenomena.
Use this if you need to create accurate, physics-informed reduced-order models (ROMs) for time-dependent PDEs, especially when you need to generalize to new parameters or perform long-term forecasting with potentially sparse data.
Not ideal if your problem does not involve time-dependent PDEs, or if you prefer purely data-driven models without enforcing physical consistency.
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Language
Python
License
MIT
Category
Last pushed
Nov 25, 2025
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