AWhat changed
Combines symbolic PDE specification, parameterized geometry, differentiated residual training and GPU-ready network architectures in a multiphysics neural-solver framework.
Scientific scope
Combines symbolic PDE specification, parameterized geometry, differentiated residual training and GPU-ready network architectures in a multiphysics neural-solver framework. Ten original authors; last-author star corresponds to contact/affiliation, not equal contribution. Inherits PINNs, Fourier features and SIREN; no duplicate invention credit. Mesh-free sampling is not exact PDE/global-error certification, and some exact divergence-free constructions introduce higher derivative cost. Original showcased use cases are steady-state; later Modulus capabilities not backdated.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Ten original authors; last-author star corresponds to contact/affiliation, not equal contribution. Inherits PINNs, Fourier features and SIREN; no duplicate invention credit. Mesh-free sampling is not exact PDE/global-error certification, and some exact divergence-free constructions introduce higher derivative cost. Original showcased use cases are steady-state; later Modulus capabilities not backdated.
Contributors
- Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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