Beyond Frontier
Pushing physics past the known
Vision · Features · Quick Start · Architecture · API · Contributing
Beyond Frontier is unified open-source infrastructure for physics: a self-evolving system that accelerates discovery, validates experiments, proposes theories, and finds the unknown.
Physics has no unified infrastructure. Calculations scatter across tools. Knowledge lives in papers. Discovery is bottlenecked by human bandwidth.
| Tool | Does | Can't Do |
|---|---|---|
| Mathematica | Symbolic math | Reason about physics |
| ChatGPT | Natural language | Rigorous derivation |
| COMSOL | Simulation | Connect to theory |
| arXiv | Store papers | Compute or validate |
DISCOVERY │ Theory proposal, gap analysis, anomalies
RESEARCH │ Validation, calculations, paper analysis
REASONING │ Neural + Symbolic + Self-Evolution
KNOWLEDGE │ Unified physics graph, equations, data
COMPUTATION │ Symbolic solvers, numerical integration
Use it at any layer. Build on top. Accelerate physics at the speed of silicon.
The core combines neural and symbolic processing:
| Component | Description |
|---|---|
| Neural Processing | Embedding-based pattern recognition and similarity matching |
| Symbolic Processing | Rule-based inference with SymPy integration |
| Hybrid Integration | Confidence-weighted combination of both approaches |
physics/
├── domains/
│ ├── classical/ # Newtonian, Lagrangian, Hamiltonian mechanics
│ ├── quantum/ # Schrodinger equation, path integrals
│ ├── fields/ # Electromagnetism, gauge theory, general relativity
│ └── statistical/ # Thermodynamics, phase transitions
├── solvers/ # Symbolic, numerical, perturbation, astrophysics solvers
└── foundations/ # Conservation laws, symmetries, constraints
561+ equations across 19 domains — classical, quantum, EM, relativity, thermodynamics, fluids, optics, nuclear, condensed matter, astrophysics, plasma/MHD, acoustics — connected by derivation chains, constants, and validity conditions.
The system can analyze and improve its own code:
- Code Analysis: AST-based understanding of codebase structure
- Safe Modification: Validated code generation with rollback
- Performance Selection: Evolutionary improvement based on metrics
| Type | Method | Use Case |
|---|---|---|
| Deductive | Modus ponens, syllogisms | Deriving conclusions from laws |
| Inductive | Pattern generalization | Discovering new relationships |
| Abductive | Best explanation inference | Hypothesis generation |
| Analogical | Structure mapping | Cross-domain transfer |
Pre-built physics simulations with conservation law validation:
- Harmonic oscillator
- Pendulum (small and large angle)
- Two-body gravitational systems
- Projectile motion with drag
Full API for integration:
- 41+ REST endpoints
- WebSocket real-time updates
- Interactive dashboard
- Python 3.10+ (3.11 recommended)
- Node.js 18+ (for the frontend)
- npm or yarn
# Clone the repository
git clone https://github.com/vastdreams/physics-ai.git
cd physics-ai
# Create virtual environment
python -m venv venv
source venv/bin/activate # Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
# Copy environment configuration
cp .env.example .envfrom core.engine import NeurosymboticEngine
# Initialize the engine
engine = NeurosymboticEngine()
# Process physics problems
result = engine.process({
"mass": 10, # kg
"velocity": 5 # m/s
})
print(result)
# Output: Calculates kinetic energy, momentum, applies physics rulesfrom physics.equations import EquationSolver
solver = EquationSolver()
# Solve F = ma for acceleration
result = solver.solve(
equation="F = m * a",
variables={'F': 100, 'm': 10},
solve_for='a'
)
print(result.solutions) # [10.0]from physics.models import HarmonicOscillator
oscillator = HarmonicOscillator(mass=1.0, spring_constant=4.0)
result = oscillator.simulate(
initial_conditions={'x': 1.0, 'v': 0.0},
t_end=10.0,
dt=0.01
)
# Energy is automatically validated for conservation
print(f"Energy conserved: {len(result.conservation_violations) == 0}")from core.reasoning import ReasoningEngineImpl, ReasoningType
reasoner = ReasoningEngineImpl(ReasoningType.DEDUCTIVE)
result = reasoner.reason([
"is_particle -> has_mass",
"electron -> is_particle",
"electron"
])
# Concludes: electron has_mass# Start the Flask API server
python -m api.app
# API available at http://localhost:5002
# Health check: GET /health
# Simulate: POST /api/v1/simulatepytest tests/ -v┌─────────────────────────────────────────────────────────────────┐
│ Beyond Frontier System │
├─────────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────────────────┐ │
│ │ Neural │ │ Symbolic │ │ Self-Evolution │ │
│ │ Component │◄─┤ Component │◄─┤ (Code Analysis/Gen) │ │
│ └──────┬──────┘ └──────┬──────┘ └───────────┬─────────────┘ │
│ │ │ │ │
│ └────────┬───────┘ │ │
│ ▼ │ │
│ ┌─────────────┐ │ │
│ │ Hybrid │◄────────────────────────┘ │
│ │ Integration │ │
│ └──────┬──────┘ │
│ │ │
│ ┌─────────────┴─────────────────────────────────────────────┐ │
│ │ Physics Domain │ │
│ │ ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────────────┐ │ │
│ │ │Classical│ │ Quantum │ │ Fields │ │ Statistical │ │ │
│ │ └─────────┘ └─────────┘ └─────────┘ └─────────────────┘ │ │
│ │ │ │ │
│ │ ┌─────────────────────┴───────────────────────────────┐ │ │
│ │ │ Solvers: Symbolic | Numerical | Differential │ │ │
│ │ └─────────────────────────────────────────────────────┘ │ │
│ └───────────────────────────────────────────────────────────┘ │
│ │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────────────────┐ │
│ │ Rules │ │ Validators │ │ API / Dashboard │ │
│ │ Engine │ │ & Loggers │ │ │ │
│ └─────────────┘ └─────────────┘ └─────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────┘
| Component | Location | Purpose |
|---|---|---|
| NeurosymboticEngine | core/engine.py |
Central orchestrator combining neural and symbolic AI |
| ReasoningEngine | core/reasoning.py |
Four types of logical reasoning |
| RuleEngine | rules/rule_engine.py |
Pattern matching with conflict resolution |
| EquationSolver | physics/equations.py |
SymPy-based symbolic equation solving |
| PhysicsModels | physics/models.py |
Simulation models with RK4 integration |
| SelfEvolution | evolution/self_evolution.py |
Code analysis and generation |
| Knowledge Graph | physics/knowledge/ |
561+ equations, constants, reasoning |
| Astro Solver | physics/solvers/astro_solver.py |
Coordinates, cosmology, orbital mechanics |
- First-Principles Foundation: Every component is grounded in mathematical principles
- Modular Architecture: Components can be used independently or composed
- Validation Everywhere: Physics constraints are checked at every step
- Self-Documenting: Chain-of-thought logging tracks all decisions
- Safe Evolution: Code changes require validation before application
| Category | Endpoints | Description |
|---|---|---|
| Simulation | /api/v1/simulate |
Run physics simulations |
| Nodes | /api/v1/nodes/* |
Code graph operations |
| Rules | /api/v1/rules/* |
Rule management |
| Evolution | /api/v1/evolution/* |
Self-evolution control |
| Reasoning | /api/v1/cot/* |
Chain-of-thought logs |
| VECTOR | /api/v1/vector/* |
Uncertainty management |
| State Graph | /api/v1/state-graph/* |
State machine operations |
| Agents | /api/v1/agents/* |
DREAM-style agent system |
curl -X POST http://localhost:5002/api/v1/simulate \
-H "Content-Type: application/json" \
-d '{
"model": "harmonic_oscillator",
"parameters": {"mass": 1.0, "spring_constant": 4.0},
"initial_conditions": {"x": 1.0, "v": 0.0},
"t_end": 10.0
}'Full API documentation: docs/API_REFERENCE.md
physics-ai/
├── core/ # Neurosymbolic engine
│ ├── engine.py # Main engine
│ ├── reasoning.py # Reasoning types
│ └── knowledge_synthesis.py
├── physics/ # Physics domain
│ ├── equations.py # Equation solver
│ ├── models.py # Simulation models
│ ├── knowledge/ # Equation graph (561+ equations, 19 domains)
│ ├── domains/ # Classical, quantum, fields, statistical
│ ├── solvers/ # Symbolic, numerical, astrophysics solvers
│ └── foundations/ # Conservation laws, symmetries
├── rules/ # Rule-based system
│ └── rule_engine.py # Pattern matching engine
├── evolution/ # Self-evolution
│ └── self_evolution.py # Code generation
├── ai/ # AI components
│ ├── agents/ # Gatekeeper, Workhorse, Orchestrator
│ ├── llm/ # LLM providers (local, API)
│ └── rubric/ # Quality gate system
├── api/ # REST API
│ ├── app.py # Flask application
│ └── v1/ # API endpoints
├── frontend/ # React dashboard
│ └── src/ # Components, pages, hooks
├── validators/ # Validation framework
├── loggers/ # Logging system
├── tests/ # Test suite
└── docs/ # Documentation
- Neurosymbolic engine with hybrid reasoning
- Four reasoning types (deductive, inductive, abductive, analogical)
- Rule engine with pattern matching
- Physics equation solver (SymPy integration)
- Simulation models with conservation validation
- REST API (41+ endpoints) + WebSocket
- Modern web dashboard
- Physics knowledge graph (561+ equations, 19 domains)
- Cross-domain reasoning (connect QM to GR)
- Hypothesis generation from knowledge gaps
- arXiv paper ingestion pipeline
- Uncertainty quantification (VECTOR framework)
- Astrophysics engine integrations (REBOUND, Astropy, galpy)
- Theory unification proposals
- Anomaly detection in physical theories
- Experimental guidance suggestions
- Multi-agent physics debate system
- Novel prediction generation
- Mathematical structure discovery
- Autonomous research assistance
- Physics breakthrough collaboration
We welcome contributions. Beyond Frontier is a community-driven project.
- Fork the repository
- Create a feature branch:
git checkout -b feature/amazing-feature - Make your changes
- Run tests:
pytest tests/ -v - Commit:
git commit -m 'feat: add amazing feature' - Push:
git push origin feature/amazing-feature - Open a Pull Request
| Area | Description | Skill Level |
|---|---|---|
| Physics Models | Add new simulation models | Intermediate |
| Reasoning | Improve reasoning algorithms | Advanced |
| Documentation | Improve docs and examples | Beginner |
| Tests | Increase test coverage | Beginner |
| API | Add new endpoints | Intermediate |
| Neural | Add transformer integration | Advanced |
| Astrophysics | Engine bridges (REBOUND, Astropy) | Intermediate |
See CONTRIBUTION_CHECKLIST.md for detailed tasks.
- Read CONTRIBUTING.md
- Follow the Code of Conduct
- Review the Security Policy
| Document | Description |
|---|---|
| Architecture | System design and principles |
| Physics Framework | Physics domain structure |
| Self-Evolution | How the AI evolves |
| API Reference | Complete API documentation |
| Feature List | All implemented features |
This project is licensed under the MIT License. See LICENSE for details.
Abhishek Sehgal — GitHub
- Inspired by neurosymbolic AI research
- DREAM architecture patterns for uncertainty management
- The open-source physics and AI communities
- Algorithms from Astropy, SunPy, and the broader computational physics ecosystem