D-IPG uses a trained Deceptron and Jacobian Composition Penalty to deliver first-order equivalent performance to damped Gauss-Newton on nonlinear inverse problems at up to 77x lower inference cost.
Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators
4 Pith papers cite this work. Polarity classification is still indexing.
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GICON combines graph message passing with example-aware positional encoding to enable in-context operator learning that outperforms classical operator learning on air quality prediction tasks across regions.
Two Kriging variants add linear PDE constraints at collocation points for better interpolation of functions satisfying those equations, tested on ODEs, harmonic PDEs, and cylinder flows.
Establishes near-optimal dimension-independent convergence rates for regularized SGD with operator-valued kernels in statistical inverse problems for operator learning.
citing papers explorer
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Local Inverse Geometry Can Be Amortized
D-IPG uses a trained Deceptron and Jacobian Composition Penalty to deliver first-order equivalent performance to damped Gauss-Newton on nonlinear inverse problems at up to 77x lower inference cost.
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Graph In-Context Operator Networks for Generalizable Spatiotemporal Prediction
GICON combines graph message passing with example-aware positional encoding to enable in-context operator learning that outperforms classical operator learning on air quality prediction tasks across regions.
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Optimal Linear Interpolation under Differential Information: application to the prediction of perfect flows
Two Kriging variants add linear PDE constraints at collocation points for better interpolation of functions satisfying those equations, tested on ODEs, harmonic PDEs, and cylinder flows.
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Learning Operators by Regularized Stochastic Gradient Descent with Operator-valued Kernels
Establishes near-optimal dimension-independent convergence rates for regularized SGD with operator-valued kernels in statistical inverse problems for operator learning.