Physics-Informed Neural Networks Seepage Modeling is a M.Tech project topic for Chemical Engineering. It gives students a clear starting point for research, implementation planning, and documentation.
Physics-Informed Neural Networks Seepage Modeling Project Details
| Abstract |
Seepage in porous media is critical across various engineering domains, including groundwater management, dam safety, and energy extraction. Traditional numerical methods like finite difference and finite element approaches are well-established but face limitations when dealing with incomplete boundary conditions, unmeasurable medium parameters, uncertain constitutive relations, or sparse observational data, often necessitating extensive mesh reconstruction and repeated simulations. Physics-informed neural networks (PINNs) offer an innovative, unified differentiable modeling paradigm to address these challenges. This framework integrates governing equations, initial and boundary conditions, observational data, and structural priors directly into the training loss function. This review comprehensively examines the evolution of PINN methodologies, encompassing advancements from joint parameter-state identification to monotonicity constraints,
domain decomposition, weak-form/conservative residuals, mixed pressure-velocity formulations, explicit fracture embedding, and neural operators. Furthermore, it categorizes PINN applications based on task type, distinguishing between forward problem solution, inverse modeling, and flow-field reconstruction, thereby providing a structured understanding of their utility in complex seepage scenarios.
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| Reference Paper |
Physics-Informed Neural Networks for Seepage Modeling in Porous Media: A Review |
| Domain |
Chemical Engineering |
| Sub-Domain |
Process Systems / Process Simulation & Control / Digital Twin |
| PDF Download |
Download / View PDF |
| Get Help |
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