RL policy loop
Use reinforcement learning to select useful training actions based on model state.
Research System / Physics-Informed ML
RL-PINNs
A research framework direction for using reinforcement learning to control adaptive training decisions in Physics-Informed Neural Networks.
Adaptive Training Control
Physics grounding
Keep the system tied to differential equations, residuals, and scientific constraints.
Training feedback
Use error signals and residual quality to guide adaptive behavior.
Research extensibility
Designed as a direction for experiments across PINN architectures and PDE families.
Framework Architecture
Parallel PINN Worker Matrix
PINN Worker 1
GPU-0 / PROCPINN Worker 2
GPU-1 / PROCPINN Worker N
GPU-N / PROCUnified Replay Buffer
(s, a, r, s')
(s, a, r, s')
(s, a, r, s')
(s, a, r, s')
MEM_GEN: 100k + 25k/cycle
Reinforcement Learning Environment
DQN Agent
Policy Optimization
Reward Engine
Convergence Tracking
Adaptive Controller
Strategy Tuning
λ1, λ2, ... λn
Effect of equations
β1, β2, LR
Optimizer parameters
Lakshay Chawla
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Data Scientist, AI researcher, entrepreneur, open source developer, and educator.