I will build physics informed neural networks and mathematical surrogates

Welcome to elite-tier, physics-constrained machine learning engineering.

Most data scientists build unconstrained black-box models that violate conservation laws, fail near kinematic boundaries, and collapse when deployed on real-world systems. I bridge the gap between heavy mathematical theory and physical metal by building robust Physics-Informed Neural Networks (PINNs) and ultra-fast mathematical surrogates.

What I bring to your project:

• Physics-Informed AI: Custom loss function formulations mapping PDEs, ODEs, and real kinematic constraints (e.g., Pacejka tire dynamics, aerodynamic boundary layers).
• High-Performance Execution: Training architectures in PyTorch tailored specifically for high-integrity prediction.
• Edge-Ready Hardware Deployment: Compiling and porting trained surrogates into zero-allocation, deterministic C++ or Rust loops built for microcontrollers (ESP32) and Jetson platforms.

Whether you are optimizing a closed-loop control system, building a digital twin simulation, or training models for safety-critical hardware, I will deliver production-grade code with full validation test harnesses.

Please message me with your mathematical constraints and dataset parameter