A numerical approach for soil microbiota growth prediction through physics-informed neural network
DOI10.1016/J.APNUM.2024.08.025MaRDI QIDQ6646501
Vincenzo Vocca, Salvatore Cuomo, Mariapia De Rosa, Francesco Piccialli, Laura Pompameo
Publication date: 2 December 2024
Published in: Applied Numerical Mathematics (Search for Journal in Brave)
PDEnumerical methodsbiologyPINNphysics-informed neural networkarbuscular mycorrhizal fungiam fungisoil microbiota growth
Artificial neural networks and deep learning (68T07) Population dynamics (general) (92D25) Spectral, collocation and related methods for initial value and initial-boundary value problems involving PDEs (65M70)
Cites Work
- Scientific machine learning through physics-informed neural networks: where we are and what's next
- Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Neural‐network‐based approximations for solving partial differential equations
- Operator splitting and discontinuous Galerkin methods for advection-reaction-diffusion problem. Application to plant root growth
- fPINNs: Fractional Physics-Informed Neural Networks
- Railway safety through predictive vertical displacement analysis using the PINN-EKF synergy
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