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I will implement physics informed neural networks pinn for pdes and simulations
Expert Developer for Local AI and Automation
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About this Gig
Struggling to bridge physical laws and deep learning? Standard neural networks often ignore the underlying physics. I provide expert implementation of Physics-Informed Neural Networks (PINNs) to solve complex scientific and engineering problems.
As a versatile technical programmer, I don't just write code, I ensure your models are scientifically valid and physics-compliant.
What I Can Solve:
- PDEs & ODEs: Navier-Stokes, Burgers, Heat, Wave, and Schrodinger equations, etc.
- Inverse Problems: Identifying unknown physical parameters from noisy sensor data.
- Digital Twins: High-fidelity, physics-based industrial models.
- Operator Learning: DeepONets and Fourier Neural Operators (FNO).
Tech Stack:
- Frameworks: DeepXDE, NVIDIA Modulus, NeuroDiffEq.
- Libraries: PyTorch, JAX, TensorFlow, FEniCS.
Why Choose This Gig?
I combine deep mathematical understanding with state-of-the-art AI. Whether you are a researcher stuck on a simulation or a business needing a robust digital twin, I deliver verified, scalable solutions.
NOTE: PLEASE MESSAGE ME before ordering to discuss your specific physical problem, PDEs, and boundary conditions.
FAQ
Can you handle custom, non-standard PDEs?
Yes. I can implement custom residual loss functions for any well-defined mathematical system, including multi-physics and coupled equations.
Do you provide the training data or do I?
PINNs are often "data-free" (using collocation points), but if you have experimental data for inverse problems, I can integrate it into the training loop.
Which framework is best for my project?
It depends on the complexity. I typically recommend DeepXDE for research and NVIDIA Modulus for large-scale industrial simulations. We can discuss this during our initial chat.
What infrastructure do you use for training physical models?
I utilize high-performance cloud GPU clusters (NVIDIA A100/H100) for training to ensure the highest precision and fastest delivery times for your physical models.
