Physics-Informed Neural Network Modeling of Biodegradable Contaminant Transport through GCL/SL Composite Liners
arXiv:2606.04392v1 Announce Type: new Abstract: This study develops a two-domain physics-informed neural network framework for contaminant transport through a GCL/SL composite liner system, in which the thin GCL layer is treated using a steady-state advection-dispersion-biodegradation formulation and the underlying soil liner is modeled as a transient transport domain. Two formulations are evaluated against analytical and finite-element reference solutions under different leachate-head conditions: a standard PINN with soft constraint enforcement (Std-PINN) and a hard-constrained PINN (H-PINN),
The increasing maturity of Physics-Informed Neural Networks (PINNs) and the urgent need for better environmental engineering solutions converge to enable more precise modeling for contaminant transport.
This development offers a more accurate method for predicting and managing biodegradable contaminant spread, crucial for environmental protection and infrastructure longevity.
The ability to model complex environmental transport phenomena with higher fidelity through PINNs provides a new tool for environmental engineers and regulatory bodies.
- · Environmental engineering firms
- · Waste management industry
- · AI research in scientific domains
- · Materials science (GCL/SL manufacturers)
- · Traditional modeling software reliant on less accurate methods
- · Sectors negatively impacted by unmitigated contaminant spread
Improved design and monitoring of landfill liners and other containment systems.
Reduced environmental remediation costs and enhanced public health outcomes due to better contaminant control.
New regulatory frameworks and insurance products based on the higher predictive accuracy of these models for environmental risks.
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Read at arXiv cs.LG