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Modeling of wave propagation in space-time dependent materials using physics-informed neural networks

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Randomized trial models wave propagation in space-time dependent materials, suggesting effective simulation methods.

To model waves in space-time dependent materials using physics-informed neural networks (PINNs).
Developed a PINN framework that satisfies governing partial differential equations (PDEs).
Considered additional field quantities and complemented the equations of motion with constitutive equations.
Implemented constitutive equations as higher-weight loss terms in the PINN.
Successfully modeled wave propagation in space-time dependent materials with no need for boundary conditions.
PINN recovered known solutions for space- and time-dependent media.
PINN demonstrated capability for general space-time dependent media with unknown solutions.

Dirk‐Jan van Manen (2025) studied this question.

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View Full Paper

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Authors

Randomized trial models wave propagation in space-time dependent materials, suggesting effective simulation methods.

To model waves in space-time dependent materials using physics-informed neural networks (PINNs).
Developed a PINN framework that satisfies governing partial differential equations (PDEs).
Considered additional field quantities and complemented the equations of motion with constitutive equations.
Implemented constitutive equations as higher-weight loss terms in the PINN.
Successfully modeled wave propagation in space-time dependent materials with no need for boundary conditions.
PINN recovered known solutions for space- and time-dependent media.
PINN demonstrated capability for general space-time dependent media with unknown solutions.

Dirk‐Jan van Manen (2025) studied this question.

View Full Paper

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