physicsnemo and physicsnemo-sym
PhysicsNeMo-Sym is a specialized extension layer that builds on top of PhysicsNeMo core, providing higher-level domain-specific abstractions and physics-informed training utilities, making them complements designed to be used together rather than alternatives.
About physicsnemo
NVIDIA/physicsnemo
Open-source deep-learning framework for building, training, and fine-tuning deep learning models using state-of-the-art Physics-ML methods
This framework helps scientists and engineers build, train, and fine-tune AI models that combine real-world data with known physics principles. It takes scientific and engineering data (like point clouds or meshes) and physics equations as input to produce predictive AI models for complex systems. Researchers and domain experts in fields like climate science or computational fluid dynamics would use this to create models that offer real-time predictions.
About physicsnemo-sym
NVIDIA/physicsnemo-sym
Framework providing pythonic APIs, algorithms and utilities to be used with PhysicsNeMo core to physics inform model training as well as higher level abstraction for domain experts
PhysicsNeMo Symbolic helps scientists and engineers integrate the fundamental laws of physics into AI models. It allows you to specify physical equations, like PDEs, and geometric constraints to inform model training. This results in AI models that are more accurate and physically consistent for simulating complex systems.
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