DeepOPiraKAN learns parameter-to-spectrum mappings via operator learning and achieves relative errors of O(10^{-6}) to O(10^{-4}) for Kerr black hole quasinormal modes up to n=7 when benchmarked against Leaver's method.
Piratenets: Physics-informed deep learning with residual adaptive networks
5 Pith papers cite this work, alongside 23 external citations. Polarity classification is still indexing.
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A hybrid MRI-PINN-resolvent framework extracts mean fields from stenotic flow measurements and identifies stationary eigenmodes in the recirculation bubble plus broadband pseudo-resonance in the shear layer.
ActNet is a new KST-based neural network that outperforms KANs and competes with MLPs in PINN benchmarks for PDE simulation tasks.
A multi-agent LLM framework, ATHENA, autonomously designs and refines numerical PDE solvers and physics-informed models, reportedly beating expert-authored baselines.
EAND-ARN combines numerical differentiation for local EP constraint enforcement with an adaptive ResNet to improve accuracy and stability in inverse ECG modeling over prior methods.
citing papers explorer
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Physics informed operator learning of parameter dependent spectra
DeepOPiraKAN learns parameter-to-spectrum mappings via operator learning and achieves relative errors of O(10^{-6}) to O(10^{-4}) for Kerr black hole quasinormal modes up to n=7 when benchmarked against Leaver's method.
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Uncovering Turbulent Dynamics in Stenotic Flows from 4D-flow MRI Measurements via Resolvent Analysis and Data Assimilation
A hybrid MRI-PINN-resolvent framework extracts mean fields from stenotic flow measurements and identifies stationary eigenmodes in the recirculation bubble plus broadband pseudo-resonance in the shear layer.
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Deep Learning Alternatives of the Kolmogorov Superposition Theorem
ActNet is a new KST-based neural network that outperforms KANs and competes with MLPs in PINN benchmarks for PDE simulation tasks.
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ATHENA: Agentic Team for Hierarchical Evolutionary Numerical Algorithms
A multi-agent LLM framework, ATHENA, autonomously designs and refines numerical PDE solvers and physics-informed models, reportedly beating expert-authored baselines.
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Numerical Differentiation-based Electrophysiology-Aware Adaptive ResNet for Inverse ECG Modeling
EAND-ARN combines numerical differentiation for local EP constraint enforcement with an adaptive ResNet to improve accuracy and stability in inverse ECG modeling over prior methods.