Physics MCP Server

# Physics MCP Server - Phases 4-8 Roadmap (Graphics-Advantaged) ## Executive Summary Building on Phases 1-3 (22 tools, GPU acceleration, comprehensive physics), this roadmap delivers **graphics-first**, **GPU-accelerated** physics computing with **interactive capabilities** and **collaborative workflows**. ## Current State ✅ - 22 tools: CAS, Plot, NLI, Units, Constants, Tensor, Quantum, StatMech - GPU acceleration layer with torch/cupy fallback - SQLite persistence and artifact storage - Cross-platform TypeScript/Python architecture ## Implementation Contracts ### Acceleration Contract ```python def tool_function(..., emit_plots=True, emit_csv=True): try: result = gpu_computation(...) # torch on device device = "cuda|mps|xpu" except (RuntimeError, MemoryError): result = cpu_fallback(...) # numpy fallback device = "cpu" return { "result": result, "artifacts": {"png_b64": ..., "csv_path": ...}, "meta": {"device": device, "cached": bool, "duration_ms": int} } ``` ### Graphics Contract - **Static**: PNG (base64) + SVG + CSV for all outputs - **Dynamic**: MP4/GIF/WebM animations + frame PNGs - **Interactive**: JSON specs for client parameter sweeps - **3D**: glTF/PLY exports for VR/AR compatibility ### Safety Caps - Timeouts: 10s default, 30s animations, 300s ML training - Memory: 160x160 surfaces, 64x64 fields, 300 frame limit - Cache: 24h plots, 1h animations, 10GB total ## Phase 4: Data I/O & External Integration (2-3 weeks) ### New Packages - `tools-data-io`: HDF5, FITS, ROOT import/export - `tools-signal`: GPU FFT, filtering, spectrograms - `tools-external`: arXiv, CERN, NASA, NIST APIs - `tools-export`: Overleaf, GitHub, Zenodo, Jupyter ### Key Features ```python def data_fft(signal, emit_plots=True): # GPU-accelerated FFT with diagnostic plots freqs, spectrum = torch_fft_or_numpy_fallback(signal) if emit_plots: # 4-panel diagnostic: time domain, magnitude, phase, spectrogram artifacts["png_b64"] = generate_fft_analysis_plot(signal, freqs, spectrum) return {"freqs": freqs, "spectrum": spectrum, "artifacts": artifacts} ``` ### Acceptance Criteria - [ ] Import HDF5/FITS/ROOT with metadata extraction - [ ] FFT + filter pipeline with GPU acceleration - [ ] External API integration with rate limiting - [ ] All artifacts cached with provenance tracking ## Phase 5: Advanced Visualization (3-4 weeks) ### Enhanced Tools - `plot_volume_3d`: GPU ray casting, marching cubes - `plot_animation`: MP4/GIF/WebM with frame limits - `plot_interactive`: Parameter sweeps + UI specs - `plot_vr_export`: glTF/PLY for VR/AR ### GPU Volume Rendering ```python def plot_volume_3d(f_expr, bounds, resolution=128): try: # GPU: torch volume sampling + marching cubes vertices, faces = gpu_isosurface_extraction(f_expr, bounds, resolution) except MemoryError: # CPU fallback with reduced resolution vertices, faces = cpu_marching_cubes(f_expr, bounds, resolution//2) return { "glb_export": export_glb(vertices, faces), # VR ready "volume_png": render_volume_view(vertices), # Preview "wireframe_png": render_wireframe(vertices) # Analysis } ``` ### Interactive Parameter Spaces ```python def plot_interactive(f_expr, param_specs, grid_size=10): # Generate parameter grid with GPU batch evaluation artifact_set = [] for params in param_grid: result = evaluate_with_params_gpu(f_expr, params) artifact_set.append({ "params": params, "plot_png": render_plot(result), "thumbnail": create_thumbnail(result) }) return { "artifact_set": artifact_set, "ui_spec": {"type": "slider_grid", "parameters": param_specs} } ``` ### Acceptance Criteria - [ ] Volume rendering with GPU acceleration + CPU fallback - [ ] Animations export MP4/GIF/WebM under frame limits - [ ] Interactive sweeps generate UI specs + cached artifacts - [ ] VR exports work in Blender/Unity viewers ## Phase 6: ML/AI Augmentation (4-5 weeks) ### New Packages - `tools-ml-symbolic`: GPU genetic programming for equation discovery - `tools-ml-surrogate`: Physics-informed neural networks (PINNs) - `tools-ml-pattern`: Computer vision for physics data - `tools-explain`: LM-powered mathematical derivations ### GPU Symbolic Regression ```python def ml_symbolic_regression(data_x, data_y, max_generations=100): try: # GPU: PyTorch genetic programming device = torch.device("cuda" if available else "cpu") population = initialize_population_gpu(1000, device) for gen in range(max_generations): fitness = evaluate_population_gpu(population, data_x, data_y) population = evolve_population_gpu(population, fitness) except MemoryError: # CPU fallback with smaller population population = genetic_programming_cpu(data_x, data_y, pop_size=250) best_expr = decode_best_individual(population) return { "symbolic_expression": str(best_expr), "latex_expression": sp.latex(best_expr), "artifacts": {"fit_analysis_png": plot_fit_analysis(data_x, data_y, best_expr)} } ``` ### Physics-Informed Neural Networks ```python def ml_physics_informed(pde_expr, boundary_conditions, epochs=1000): try: # GPU PINN training device = torch.device("cuda" if available else "cpu") net = PINN([2, 50, 50, 50, 1]).to(device) for epoch in range(epochs): physics_loss = compute_pde_residual_gpu(net, pde_expr) boundary_loss = compute_boundary_loss_gpu(net, boundary_conditions) total_loss = physics_loss + 10 * boundary_loss total_loss.backward() except MemoryError: # CPU fallback with smaller network net, loss_history = train_pinn_cpu(pde_expr, boundary_conditions, hidden=[25, 25], epochs=epochs//2) return { "solution_grid": net.predict(test_points), "artifacts": {"solution_png": plot_pinn_solution(net)} } ``` ### LM-Powered Explanations ```python def explain_derivation(mathematical_statement): # Query LM Studio for step-by-step derivation prompt = f"Derive step-by-step: {mathematical_statement}" response = query_lm_studio(prompt, max_tokens=2000) # Extract LaTeX equations and generate visual steps latex_equations = extract_latex_equations(response.text) step_images = [render_latex_to_image(eq) for eq in latex_equations] return { "derivation_text": response.text, "latex_equations": latex_equations, "artifacts": {"derivation_pdf": create_derivation_document(response.text)} } ``` ### Acceptance Criteria - [ ] Symbolic regression finds correct expressions on test data - [ ] PINNs solve heat/wave/Poisson equations accurately - [ ] Pattern recognition works on physics image datasets - [ ] LM explanations generate coherent derivations - [ ] All ML tools use GPU with graceful CPU fallback ## Phase 7: Distributed & Collaborative (4-5 weeks) ### New Packages - `tools-distributed`: SLURM, K8s, AWS Batch job submission - `tools-collaborative`: Multi-user sessions, real-time sync - `tools-versioning`: Git-like artifact versioning - `tools-notebook`: Signed lab notebooks with peer review ### Remote Execution ```python def job_submit_slurm(computation_spec, resources): # Generate SLURM script with GPU allocation job_script = generate_slurm_script(computation_spec, resources) # Submit and track job job_id = submit_slurm_job(job_script) # Register for artifact retrieval register_remote_job(job_id, computation_spec) return {"job_id": job_id, "status": "queued", "estimated_runtime": "2h"} ``` ### Artifact Versioning ```python def artifact_versioning(artifact_path, operation="commit"): # Git-like versioning for artifacts if operation == "commit": hash_id = compute_artifact_hash(artifact_path) store_artifact_version(artifact_path, hash_id) return {"version": hash_id, "size": get_file_size(artifact_path)} elif operation == "diff": return compare_artifact_versions(artifact_path, version1, version2) ``` ### Collaborative Sessions ```python def session_share(session_id, collaborators, permissions="read"): # Enable multi-user access to session for user in collaborators: grant_session_access(session_id, user, permissions) notify_user_session_shared(user, session_id) return {"shared_with": collaborators, "access_url": generate_session_url(session_id)} ``` ### Acceptance Criteria - [ ] SLURM jobs submit and return artifacts with provenance - [ ] Multi-user sessions sync in real-time - [ ] Artifact versioning tracks dependencies and lineage - [ ] Lab notebooks support digital signatures and peer review ## Phase 8: Unified Digital Physics Lab (5-6 weeks) ### Orchestration Tools - `experiment_define`: NL → workflow DAG with graphics outputs - `experiment_run`: Execute DAG with GPU parallelization - `experiment_publish`: Paper-like PDF with figures + BibTeX - `experiment_collaborate`: Share workflows + artifacts ### Experiment Definition ```python def experiment_define(natural_language_description): # Parse NL into computational workflow DAG workflow_dag = parse_experiment_description(natural_language_description) # Each node declares visual outputs for node in workflow_dag.nodes: node.outputs = { "static_plots": ["png", "svg"], "animations": ["mp4"] if node.is_time_dependent else [], "data_exports": ["csv", "hdf5"], "interactive": ["ui_spec"] if node.has_parameters else [] } return {"dag": workflow_dag, "estimated_runtime": estimate_dag_runtime(workflow_dag)} ``` ### Experiment Execution ```python def experiment_run(workflow_dag, resources="auto"): # Parallelize GPU-friendly nodes gpu_nodes = identify_gpu_parallelizable_nodes(workflow_dag) # Execute with resource allocation results = {} for node in topological_sort(workflow_dag): if node in gpu_nodes and gpu_available(): result = execute_node_gpu(node, resources) else: result = execute_node_cpu(node) results[node.id] = result # Store artifacts with lineage store_node_artifacts(node.id, result.artifacts, dependencies=get_node_dependencies(node)) return {"results": results, "total_artifacts": count_artifacts(results)} ``` ### Publication Generation ```python def experiment_publish(experiment_results, title, authors): # Generate paper-like PDF with auto-generated figures document = create_latex_document(title, authors) # Add figures with auto-generated captions for node_id, result in experiment_results.items(): if "png_b64" in result.artifacts: caption = generate_figure_caption(node_id, result.meta) document.add_figure(result.artifacts["png_b64"], caption) # Add methods section from tool metadata methods_section = generate_methods_from_metadata(experiment_results) document.add_section("Methods", methods_section) # Compile to PDF pdf_path = compile_latex_to_pdf(document) return { "pdf_path": pdf_path, "figure_count": count_figures(document), "bibtex": generate_bibtex_from_tools(experiment_results) } ``` ### End-to-End Example ```python # Define experiment in natural language experiment = experiment_define(""" Compute the hydrogen 2p orbital wavefunction using quantum mechanics, visualize it as a 3D volume rendering and 2D cross-sections, compare with experimental data from NIST, and generate publication-quality figures. """) # Execute with GPU acceleration where possible results = experiment_run(experiment.dag, resources={"gpu": True, "memory": "16GB"}) # Publish as paper with auto-generated figures paper = experiment_publish(results, title="Hydrogen 2p Orbital Visualization and Analysis", authors=["Physics MCP User"]) ``` ### Acceptance Criteria - [ ] Natural language → executable workflow DAG - [ ] GPU parallelization of compatible nodes - [ ] Auto-generated papers with figures and captions - [ ] End-to-end hydrogen orbital example works - [ ] Collaboration features enable multi-user workflows ## Implementation Timeline **Total Duration**: 18-22 weeks (4.5-5.5 months) | Phase | Duration | Key Deliverables | |-------|----------|------------------| | Phase 4 | 2-3 weeks | Data I/O, FFT, External APIs | | Phase 5 | 3-4 weeks | Volume rendering, Animations, VR export | | Phase 6 | 4-5 weeks | ML/AI tools, Symbolic regression, PINNs | | Phase 7 | 4-5 weeks | Distributed computing, Collaboration | | Phase 8 | 5-6 weeks | Unified lab, Experiment orchestration | ## Success Metrics ### Technical Metrics - **Tool Count**: 22 → 60+ tools across all domains - **GPU Utilization**: >80% of compute-intensive operations accelerated - **Cache Hit Rate**: >60% for repeated operations - **Memory Efficiency**: No OOM crashes under safety caps ### User Experience Metrics - **Graphics Quality**: Publication-ready figures from all tools - **Interactivity**: Parameter sweeps with real-time preview - **Collaboration**: Multi-user sessions with conflict resolution - **Publication**: End-to-end paper generation in <1 hour ### Scientific Impact Metrics - **Accuracy**: ML tools match or exceed literature benchmarks - **Performance**: 10x speedup on GPU vs CPU for large problems - **Reproducibility**: All experiments fully reproducible with versioning - **Adoption**: Integration with major physics workflows ## Risk Mitigation ### Technical Risks - **GPU Memory**: Automatic fallback + memory monitoring - **Model Accuracy**: Validation against known solutions - **Performance**: Profiling + optimization at each phase ### Integration Risks - **API Changes**: Version pinning + compatibility layers - **Platform Support**: Continuous testing on Windows/Linux/macOS - **Scaling**: Load testing with realistic physics workloads ### User Adoption Risks - **Learning Curve**: Comprehensive docs + interactive tutorials - **Migration**: Backward compatibility + migration tools - **Support**: Community forum + expert consultation --- **Ready to proceed with Phase 4 implementation?** 🚀