Welcome to my first project deep-dive. Coming from a chemical engineering background, standard black-box machine learning models often fall short when dealing with strict physical laws.

The Challenge

Standard neural networks don’t inherently understand thermodynamics or conservation of mass.

The Solution: PINNs

By embedding physical laws directly into the loss function of a neural network, we can create models that are both data-driven and physically consistent.

Here is a quick conceptual example of how we might define a custom physics-informed loss function in PyTorch:

import torch
import torch.nn as nn

def physics_loss(prediction, target, physical_constraint):
    # Standard data loss
    mse_loss = nn.MSELoss()(prediction, target)
    
    # Physics penalty
    penalty = torch.mean(torch.square(physical_constraint))
    
    return mse_loss + (0.1 * penalty)