PNOT combines graph attention on boundary heat flux with a physics-aware neural operator and gradient-constrained loss to reconstruct divertor temperature fields for real-time fusion control.
DPOT: Auto-regressive denoising operator transformer for large-scale PDE pre-training
10 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
verdicts
UNVERDICTED 10representative citing papers
Therm-FM adapts a pretrained PDE foundation model using thermal-equivalent multi-fidelity training to achieve up to 10.6x lower error in 3D-IC thermal simulation with under 20% of typical training data and strong cross-design transfer.
LGS pretrained on 2.5M trajectories across 16 systems matches deterministic baselines at one step and halves 20-step error while using far less compute and adapting to held-out higher-resolution flows.
CHOP reduces relative inference error on OOD operator tasks for scalar conservation laws and mean-field control by composing frozen ICON with explicit closed-form elementary operators that remain interpretable.
AutoPDE maintains an explicit solver strategy through PDE analysis, numerical method selection, and adaptive tuning, achieving 54.5% pass rate on PDE Agent Bench, 14.2 points above the strongest baseline.
WaveLiT combines wavelet tokenization, linear attention, and multiscale pyramids to produce parameter-efficient neural PDE solvers that match much larger models on TheWell benchmarks.
ARC-STAR reduces velocity rollout error by at least 36x over raw Poseidon across all tested regime cells via auditable global and local correction stages on five flow benchmarks.
Flow Marching jointly samples noise and physical time to learn a velocity field for generative PDE modeling, paired with a latent autoencoder and efficient transformer for large-scale pretraining on 2.5M trajectories.
SIGS is a neuro-symbolic framework that discovers analytical solutions to PDEs by generating grammar-constrained expressions, embedding them in a topology-regularised latent manifold, and refining structure and coefficients against the PDE residual and boundary/initial conditions.
A replay-based continual learning strategy for physics-informed neural operators mitigates catastrophic forgetting on prior physical problems while enabling efficient adaptation to new data using only physical constraints.
citing papers explorer
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Temperature Field Reconstruction of Tungsten Monoblock Divertor on EAST using Physics-aware Neural Operator Transformer
PNOT combines graph attention on boundary heat flux with a physics-aware neural operator and gradient-constrained loss to reconstruct divertor temperature fields for real-time fusion control.
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Therm-FM: Foundation Model is ALL YOU NEED for 3D-ICs Thermal Simulation
Therm-FM adapts a pretrained PDE foundation model using thermal-equivalent multi-fidelity training to achieve up to 10.6x lower error in 3D-IC thermal simulation with under 20% of typical training data and strong cross-design transfer.
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Latent Generative Solvers for Generalizable Long-Term Physics Simulation
LGS pretrained on 2.5M trajectories across 16 systems matches deterministic baselines at one step and halves 20-step error while using far less compute and adapting to held-out higher-resolution flows.
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Harness In-Context Operator Learning with Chain of Operators
CHOP reduces relative inference error on OOD operator tasks for scalar conservation laws and mean-field control by composing frozen ICON with explicit closed-form elementary operators that remain interpretable.
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AutoPDE: Reliable Agentic PDE Solving via Explicitly Represented Solver Strategies
AutoPDE maintains an explicit solver strategy through PDE analysis, numerical method selection, and adaptive tuning, achieving 54.5% pass rate on PDE Agent Bench, 14.2 points above the strongest baseline.
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Small Models, Strong Priors: Architectural Inductive Bias for Parameter-Efficient Neural PDE Solvers
WaveLiT combines wavelet tokenization, linear attention, and multiscale pyramids to produce parameter-efficient neural PDE solvers that match much larger models on TheWell benchmarks.
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ARC-STAR: Auditable Post-Hoc Correction for PDE Foundation Models
ARC-STAR reduces velocity rollout error by at least 36x over raw Poseidon across all tested regime cells via auditable global and local correction stages on five flow benchmarks.
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Flow marching for a generative PDE foundation model
Flow Marching jointly samples noise and physical time to learn a velocity field for generative PDE modeling, paired with a latent autoencoder and efficient transformer for large-scale pretraining on 2.5M trajectories.
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Neuro-Symbolic AI for Analytical Solutions of Differential Equations
SIGS is a neuro-symbolic framework that discovers analytical solutions to PDEs by generating grammar-constrained expressions, embedding them in a topology-regularised latent manifold, and refining structure and coefficients against the PDE residual and boundary/initial conditions.
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Replay-Based Continual Learning for Physics-Informed Neural Operators
A replay-based continual learning strategy for physics-informed neural operators mitigates catastrophic forgetting on prior physical problems while enabling efficient adaptation to new data using only physical constraints.