AI Decision System
Replayable, offline-verifiable records for AI-assisted go/no-go decisions: experiment promotions, typed model answers and human-approved actions.
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一分钟完成验证Differentiable network modelling of physical systems, built on PyTorch
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取自仓库 README
正在加载完整 README。 以下为已保存的摘录。
noodl physics is an open-source framework for differentiable network modelling of physical
systems, built on PyTorch. It combines graph
topology, conservation laws, and modular descriptions of physical processes to model flows,
storage, transport, and reactions within a common framework. Different physical systems can be
composed and coupled through shared network structures, while differentiable solvers support
simulation, parameter estimation, and optimisation.查看完整 README ↗目前无法加载完整 README。请通过原文链接阅读。 以下为已保存的摘录。
noodl physics is an open-source framework for differentiable network modelling of physical
systems, built on PyTorch. It combines graph
topology, conservation laws, and modular descriptions of physical processes to model flows,
storage, transport, and reactions within a common framework. Different physical systems can be
composed and coupled through shared network structures, while differentiable solvers support
simulation, parameter estimation, and optimisation.查看完整 README ↗