OnlyDense learns neural basis functions to approximate particle system states in a low-dimensional linear Hilbert subspace, unifying projection-based ROM with deep learning for accurate SPH dynamics modeling with 32 bases at R²>0.99.
arXiv preprint arXiv:2209.14855 , year=
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A finite element-guided physics-informed operator learning framework learns solution operators for coupled multiphysics PDEs, enabling discretization-independent predictions on arbitrary domains without labeled data.
Physics-conforming Latent Twins learns encoder-decoder pairs and latent flow maps that satisfy physical principles by design via constraint transfer and algebraic conditions on invariants and dissipation.
A feed-forward parameter-to-latent mapping plus a parameter-conditioned Neural ODE decouples space, time, and PDE parameters in a single physics-informed surrogate.
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citing papers explorer
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OnlyDense: Reduced-Order Modeling for Lagrangian simulation
OnlyDense learns neural basis functions to approximate particle system states in a low-dimensional linear Hilbert subspace, unifying projection-based ROM with deep learning for accurate SPH dynamics modeling with 32 bases at R²>0.99.
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Tackling multiphysics problems via finite element-guided physics-informed operator learning
A finite element-guided physics-informed operator learning framework learns solution operators for coupled multiphysics PDEs, enabling discretization-independent predictions on arbitrary domains without labeled data.
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Physics-conforming Latent Twins
Physics-conforming Latent Twins learns encoder-decoder pairs and latent flow maps that satisfy physical principles by design via constraint transfer and algebraic conditions on invariants and dissipation.
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Disentangled Latent Dynamics Manifold Fusion for Solving Parameterized PDEs
A feed-forward parameter-to-latent mapping plus a parameter-conditioned Neural ODE decouples space, time, and PDE parameters in a single physics-informed surrogate.
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A Simple but Efficient Transformer-Based Physics-Informed Neural Network for Incompressible Navier--Stokes Equations
PhysicsFormer applies a lightweight Transformer PINN with pseudo-sequential representations to convection, Burgers, lid-driven cavity, and inverse Navier-Stokes problems, reporting near-zero error in parameter identification and flow reconstruction from sparse noisy data.