README.md

metadata

license: mit
tags:
  - aerospace
  - physics-informed-neural-network
  - pinn
  - pytorch
  - surrogate-model
  - alrithm
library_name: pytorch

🚀 ALRITHM-Astro

ALRITHM-Astro is an AI-powered Aerospace Engineering Copilot designed to simulate rocket flight dynamics and predict Maximum Dynamic Pressure (Max Q). It combines a custom-trained Physics-Informed Neural Network (PINN) with a natural language interface.

🔗 Try the Live Demo Here

🏛️ Powered by Alrithm

This project is fundamentally built upon and inspired by the vision of Alrithm.

• Data Source: The current model version is trained on a specific subset of the alr-aerospace-platform-0.2 dataset (targeting the max_q_kpa quantity), fetched via the official Alrithm API.
• Philosophy: Alrithm provides rigorously verified, high-fidelity aerospace data. ALRITHM-Astro serves as a demonstration of how developers can leverage Alrithm's verified data streams to build real-time, physics-informed AI applications.

✨ Features

• Natural Language Processing: Understands complex aerospace queries using Groq's LLMs (openai/gpt-oss-120b).
• Physics-Informed Prediction: Calculates Max Q with high precision using a custom PyTorch Neural Network.
• Interactive 3D Visualization: Renders the rocket's 3D trajectory in real-time using Plotly.js.
• Full-Stack Architecture: FastAPI backend (Render) + Vanilla JS frontend (Vercel).

🧠 Model Details (Version 0.2 - Max Q Specialist)

• Architecture: Multi-Layer Perceptron (MLP) with 256 hidden units and SiLU activation.
• Input Features: 13 engineered physical features (e.g., Burnout Velocity, Thrust-to-Weight Ratio, Mass Ratio).
• Output: Maximum Dynamic Pressure (max_q_kpa).
• Training: 5-Fold Cross-Validation (Fine-tuned specifically on the max_q_kpa subset of the Alrithm dataset).

⚠️ Current Limitation: This v0.2 model is a Single-Task surrogate model. It is currently specialized only for max_q_kpa. It has not yet been trained on the entire 4600+ dataset across all task types.

🗺️ Roadmap (Next Steps)

• Phase 1: Build the End-to-End Pipeline (Data -> Neural Network -> FastAPI -> Vercel).
• Phase 2: Train a Single-Task Model for max_q_kpa.
• Phase 3: Implement Multi-Task Learning (MTL) using the full 4600+ alr-aerospace-platform-0.2 dataset to predict all quantities (Apogee, Burnout Speed, etc.) simultaneously.

🛠️ How to use this model

You can load this model directly using PyTorch:

import torch
import torch.nn as nn

class PureMLPModel_Deep(nn.Module):
    def __init__(self, in_dim=13, hidden=256):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(in_dim, hidden), nn.SiLU(),
            nn.Linear(hidden, hidden), nn.SiLU(),
            nn.Linear(hidden, hidden), nn.SiLU(),
            nn.Linear(hidden, 1),
        )
    def forward(self, x):
        return self.net(x).squeeze(-1)

# 1. Load the ALRITHM-Astro model weights
checkpoint = torch.load("alrithm_astro_model.pth", map_location="cpu")

# 2. Initialize the architecture and load weights
model = PureMLPModel_Deep()
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()

# 3. Retrieve the scalers for data normalization
scaler_x = checkpoint['scaler_x']
scaler_y = checkpoint['scaler_y']

print("✅ ALRITHM-Astro model loaded successfully!")