I will develop physics informed neural networks

Need a physics-informed machine learning model for an engineering or scientific problem?

I develop Physics-Informed Neural Networks (PINNs) and Scientific Machine Learning models that combine governing physical equations with neural networks for accurate and computationally efficient modelling.

With a strong background in applied mathematics, mechanical engineering, numerical modelling, CFD, and heat transfer, I can help translate physical problems into reliable computational models.

My services include:

Physics-Informed Neural Networks (PINNs)

ODE and PDE modelling

Forward physics problems

Parameter estimation and inverse modelling

Scientific machine learning

Surrogate modelling for engineering simulations

Heat-transfer and fluid-flow applications

Model training and optimization

Validation against numerical or experimental data

Python-based implementation and result visualization

Deliverables can include documented source code, trained models, validation results, plots, and technical interpretation depending on your selected package.

Please contact me before ordering so I can review the governing equations, boundary conditions, available data, and expected outputs.