Automatic differentiation might bring about numerical instability in the solution of partial differential equations (PDEs). Therefore, to address this challenge, an MTN-PINN …
Physics–Informed Neural Networks (PINNs) have demonstrated efficacy in solving both forward and inverse problems for nonlinear partial differential equations (PDEs). However, they …
The Ekman equation is a fundamental model for describing the wind stress response in the ocean's upper layer,with its key parameters—the vertical eddy viscosity coefficient (VEVC) …
Variations in ocean mixed layer depth (MLD) show a significant impact on energy balance in the global climate systems and marine ecosystems. So far, the accuracy of modeling MLD, …
Estimating ocean subsurface thermohaline information from satellite data is vital for understanding ocean dynamics and El Niño. This paper presents a double-output Residual Neural …