🧮 PINN Enterprise Platform

AI-Powered Physics Simulations with CopilotKit-style Research Canvas UI

A complete, production-ready enterprise platform for Physics-Informed Neural Networks (PINNs) featuring a CopilotKit-inspired research canvas UI, RAG-powered AI code generation, and global serverless deployment.

🌟 Live Demo

🎨 Research Canvas UI: Try the Interactive Demo
📚 API Documentation: Explore the API
🚀 Production Deployment: Coming soon at api.ensimu.space

Architecture Principles

Core Design Philosophy

• Hybrid Serverless: Lambda for coordination, containers for computation
• Event-Driven: Asynchronous messaging for all components
• GPU-Optimized: Intelligent GPU resource allocation for PINN training/inference
• Cost-Efficient: Pay-per-use with automatic scaling and resource optimization
• Fault-Tolerant: Graceful degradation and automatic retries

Quick Start

# Install dependencies
npm install -g serverless
pip install -r requirements.txt

# Deploy the platform
serverless deploy --stage prod

# Build and deploy training containers
./scripts/deploy-containers.sh

# Deploy infrastructure
./scripts/deploy-infrastructure.sh

Architecture Components

1. API Gateway & Orchestration - Main entry point and workflow coordination
2. PINN Problem Analyzer - Intelligent problem analysis and architecture recommendation
3. ECS Training Service - GPU-accelerated PINN training with DeepXDE
4. Fast Inference Handler - Real-time inference with model caching
5. Model Deployment - SageMaker integration for production inference
6. Monitoring & Optimization - Cost optimization and performance monitoring

Supported Physics Domains

• Heat Transfer (Diffusion equations)
• Fluid Dynamics (Navier-Stokes equations)
• Structural Mechanics (Elasticity equations)
• Electromagnetics (Maxwell equations)
• Wave Propagation (Wave equations)

Features

• Automatic PINN Architecture Selection: Based on problem complexity and physics domain
• Hybrid Compute Strategy: Lambda for coordination, ECS/Batch for heavy computation
• Real-time Inference: Sub-second inference with cached models
• Cost Optimization: Intelligent resource scaling and cleanup
• Production Monitoring: CloudWatch dashboards and custom metrics
• Multi-GPU Support: Automatic GPU allocation for training workloads

API Endpoints

• POST /pinn/solve - Submit physics problem for PINN solution
• GET /pinn/status/{workflow_id} - Check workflow status
• GET /pinn/results/{workflow_id} - Retrieve simulation results
• POST /pinn/inference/{workflow_id} - Real-time inference

Cost Estimates

Typical costs for different problem types:

• Simple heat transfer: $0.10 - $0.50 per solution
• Complex fluid dynamics: $2.00 - $10.00 per solution
• Real-time inference: $0.001 - $0.01 per request

License

MIT License - see LICENSE file for details.