An SO(3)-equivariant neural network predicts the full 6×6 grand resistance matrix for microparticles in Stokes flow, outperforming traditional scalar drag correlations by preserving orientation dependence and rotational response.
See LICENSE fileStokes flowresistance tensorequivariant neural network
This paper develops a structure-informed data-driven ROM for transport-dominated systems governed by scalar hyperbolic conservation laws, using the kinetic defect formulation to separate characteristic transport from entropy defects localized on shock manifolds.
See LICENSE filereduced-order modelinghyperbolic conservation lawsshock capturing
Introduces mollified-sharp decomposition for probabilistic regularization of moving shocks in parametric reduced-order models, enhancing shock-capturing capabilities in CFD applications.
See LICENSE fileshock capturereduced-order modelprobabilistic regularization
This paper proposes an improved periodic activation using complex exponential functions generating paired sine-cosine outputs for Physics-Informed Neural Networks, demonstrating superior performance over sine-activated MLPs in reconstructing temperature fields from sparse velocity data in convective flows.
See LICENSE filePINNsperiodic activationconvective flows
