MVNN learns measure-dependent drift terms in McKean-Vlasov equations from particle data using an embedding network, with proofs of well-posedness, propagation of chaos, and universal approximation under low-dimensional assumptions.
Pdeformer-2: A versatile foundation model for two-dimensional partial differential equations
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Tadpole is a pre-trained autoencoder foundation model for 3D PDEs that learns transferable representations from online-generated data and supports efficient fine-tuning for dynamics prediction and other tasks.
Self-supervised neural operator uses Bayesian PINNs to generate training data and a Transformer to learn PDE operators, achieving high accuracy on 1D/2D reaction-diffusion and fluid vibration problems with optional lightweight finetuning.
Multiple Neural Operators achieve near-optimal approximation and generalization rates for multi-task operator learning, matching single-task scaling laws and performing similarly to a multi-task DeepONet extension.
jNO introduces a unified JAX tracing system for data-driven and physics-informed neural operator training that compiles domains, residuals, losses, and diagnostics into one pipeline.
citing papers explorer
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MVNN: A Measure-Valued Neural Network for Learning McKean-Vlasov Dynamics from Particle Data
MVNN learns measure-dependent drift terms in McKean-Vlasov equations from particle data using an embedding network, with proofs of well-posedness, propagation of chaos, and universal approximation under low-dimensional assumptions.
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Tadpole: Autoencoders as Foundation Models for 3D PDEs with Online Learning
Tadpole is a pre-trained autoencoder foundation model for 3D PDEs that learns transferable representations from online-generated data and supports efficient fine-tuning for dynamics prediction and other tasks.
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Self-supervised neural operator for solving partial differential equations
Self-supervised neural operator uses Bayesian PINNs to generate training data and a Transformer to learn PDE operators, achieving high accuracy on 1D/2D reaction-diffusion and fluid vibration problems with optional lightweight finetuning.
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Multiple Neural Operators Achieve Near-Optimal Rates for Multi-Task Learning
Multiple Neural Operators achieve near-optimal approximation and generalization rates for multi-task operator learning, matching single-task scaling laws and performing similarly to a multi-task DeepONet extension.
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jNO: A JAX Library for Neural Operator and Foundation Model Training
jNO introduces a unified JAX tracing system for data-driven and physics-informed neural operator training that compiles domains, residuals, losses, and diagnostics into one pipeline.