MH-PINN compactifies unbounded domains with mapping and enforces wave boundary conditions through network architecture for efficient, accurate simulations.
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A single trained parameterized NA-PINN coupled to FDM delivers low-error solutions for gravity-driven draining across multiple time steps and initial conditions without retraining or simulation data.
ATHENA introduces an agentic team framework that autonomously manages the end-to-end computational research lifecycle via a knowledge-driven HENA loop to achieve validation errors of 10^{-14} in scientific computing and machine learning tasks.
citing papers explorer
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Mapping-based Hard-constrained Physics-Informed Neural Networks for unbounded wave problems
MH-PINN compactifies unbounded domains with mapping and enforces wave boundary conditions through network architecture for efficient, accurate simulations.
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A Numerical Method for Coupling Parameterized Physics-Informed Neural Networks and FDM for Advanced Thermal-Hydraulic System Simulation
A single trained parameterized NA-PINN coupled to FDM delivers low-error solutions for gravity-driven draining across multiple time steps and initial conditions without retraining or simulation data.
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ATHENA: Agentic Team for Hierarchical Evolutionary Numerical Algorithms
ATHENA introduces an agentic team framework that autonomously manages the end-to-end computational research lifecycle via a knowledge-driven HENA loop to achieve validation errors of 10^{-14} in scientific computing and machine learning tasks.