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microstructure-evolution

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SFETNI / PINNs_MPF--a-Physics-Informed-Neural-Network-for-Multi-Phase-Field-problems

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PINNs-MPF is a comprehensive framework designed for simulating interface dynamics using Physics-Informed Neural Networks (PINNs). Leveraging machine learning techniques, this framework offers an efficient implementation for the multi-phase-field model

deep-learning grain-growth tesnorflow grain-boundaries scientific-machine-learning computational-materials-science physics-informed-neural-networks multiphase-field microstructure-evolution
  • Updated Aug 30, 2026
  • Python

SFETNI / PINN-Phase

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PINN-Phase: a physics-informed neural time integrator for curvature-driven multiphase-field evolution, built for long-horizon rollouts, transfer across unseen microstructures (2D&3D), and extension to new simulation settings. A reusable complement to traditional phase-field solvers for repeated studies, benchmarking, and collaborative development.

deep-learning materials pytorch computational-physics gpu-computing phase-field grain-growth microstructure pinn grain-boundaries scientific-machine-learning computational-materials-science physics-informed-neural-networks multiphase-field microstructure-evolution
  • Updated Sep 2, 2026
  • Python

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