computational physics

Jeffrey Emanuel ✔ @doodlestein · 19h So if I manage to actually build this thing, who gets the plaudits? Me or Fable/Anthropic? Can we agree it's me (lol)? That would be like crediting Aladdin with the works of the Genie. But I DID have to ask for something odd, in a very special way... claude.ai/public/artifac... [Embedded document image, titled "COMPREHENSIVE PLAN FOR FRANKENSIM", subtitle: "A single, memory-safe Rust continuum for computational geometry, physics, optimization, and rendering — designed from a blank slate for Apple Silicon and many-core x86, built on the Franken constellation (asupersync, FrankenSQLite, FrankenNumpy, FrankenTorch, FrankenScipy, FrankenPandas, FrankenNetworkx), with zero other runtime dependencies."] 0. How to read this document This is a design plan, not a survey. Every mechanism described here is chosen because it is load-bearing for the mission: given a physics-based objective and constraints, synthesize the geometry that optimizes it — faster, more correctly, and more verifiably than any existing system, on commodity many-core CPUs, in pure safe Rust. Ambition is calibrated with three tags used throughout: • [S] Solid — established mathematics and engineering; the work is implementation excellence, not research risk. • [F] Frontier — published research from roughly the last decade that no mainstream system has productized; real engineering risk, enormous payoff. • [M] Moonshot — novel synthesis proposed here for the first time (to my knowledge); prototyped behind feature flags, promoted only after the Gauntlet (§13) validates it. The mix is deliberate: the spine of FrankenSim is [S], the leapfrog features are [F], and a handful of [M] bets are what make the system unlike anything else. Nothing tagged [M] sits on the critical path of the roadmap. 1. Thesis: why a blank slate wins Every existing pipeline for "optimize a shape against physics" is an archipelago: OpenCASCADE or a B-rep kernel for geometry, gmsh or a proprietary mesher for discretization, an FEM/CFD code (FEniCS, MFEM, deal.II, OpenFOAM, SU2, COMSOL, Abaqus) for physics, SciPy/NLopt/Dakota for optimization, ParaView for looking at the wreckage. Each island is excellent. The water between them is where everything drowns: 1. Derivatives don't cross boundaries. The CAD kernel doesn't know the mesh's sensitivity to a control point; the solver's adjoint dies at the mesher; the optimizer sees a noisy black box and falls back to finite differences or pure evolution. 2. Error bounds don't cross boundaries. Geometry tolerance, meshing error, discretization error, solver tolerance, statistical noise — nobody composes them. You get a number with no pedigree. 3. Provenance doesn't exist. Six weeks into a design study, nobody can reconstruct which mesh, which solver settings, and which random seed produced the Pareto point the client liked. 4. Cancellation doesn't exist. An optimizer that discovers a candidate is hopeless after 10% of its simulation cannot claw back the other 90% of the compute — the process model is "run to completion or kill -9." 5. The hardware is wasted. These codebases predate 96-core CCD-partitioned CPUs and 546 GB/s unified-memory laptops; they are MPI-shaped or single-threaded-with-OpenMP-sprinkles, allergic to work stealing, and dependent on BLAS binaries tuned for a different decade. [cut off]

``` Jeffrey Emanuel ✔ @doodlestein · 1h This is so nuts. The ask here was like the Manhattan Project in terms of the scope and the sheer amount of expertise across so many domains that are required to design such a system. These models are the first entities in history that have all this understanding in ONE 🧠: [Embedded thinking-trace screenshot, continuing text, greyed/faded at top:] The real power comes from tying adaptivity directly to the optimization objective—refine the mesh only where it actually affects the design goal. For the solver, I'm avoiding global sparse matrix assembly entirely, using matrix-free operators with sum-factorization for high-order elements to squeeze near-peak performance on modern hardware. I'd combine p-multigrid with algebraic multigrid for coarse solves, then use Krylov methods with matrix-free preconditioners. For non-uniform grids, adaptive octrees with wavelet-based compression handle the SDF and voxel physics efficiently. On the fluid side, for something like the vessel spout example, I'd implement incompressible Navier-Stokes with both grid-based FEEC-stabilized methods and Lattice Boltzmann as options—LBM is naturally parallel and works beautifully with voxel geometry, especially on many-core systems. Free-surface handling via volume-of-fluid or level set rounds out the capability. For structures, the building frame case calls for mixed formulations using FEEC with elements like TDNNS or weakly imposed symmetry, plus reduced models for beams and shells through Cosserat rods or isogeometric analysis. The real win here is IGA—using the same B-spline basis for both geometry and analysis means the CAD representation and the physics solver share function spaces directly, eliminating the mesh conversion step entirely. On the seismic side, I'm handling stochastic loads through random field expansions and polynomial chaos methods to propagate uncertainty, then optimizing the CVaR of the response for robustness. Contact and collision detection uses either convex decomposition with GJK/EPA or signed distance fields for penetration depth. Everything gets adjoint treatment—discrete adjoints for gradient computation through the PDE solve, with checkpointing strategies for time-dependent problems. Now I'm moving into the optimization layer itself, setting up both gradient-based methods via adjoints and automatic differentiation, plus exploring how to integrate these into a differentiable framework. Morphable signed distance functions and neural implicit geometry (training a small MLP directly as the shape representation, similar to DeepSDF but for engineering problems) with differentiable rendering. Then there's Riemannian optimization — design spaces are often manifolds like rotations or Grassmann manifolds for modal subspaces, so I'd use Riemannian ADAM or trust-region methods; shape space itself is an infinite-dimensional Riemannian manifold with Michor–Mumford metrics, enabling geodesic interpolation between designs. Optimal transport and Wasserstein distances also come into play for comparing designs and blending them via barycenters, plus entropic regularization for topology optimization. I'm also considering multi-objective optimization with Pareto fronts via evolutionary algorithms and hypervolume indicators, e-process racing with successive halving and Hyperband for early elimination of bad designs with statistical guarantees, and surrogate models — Gaussian processes and neural operators like Fourier Neural Operators trained on simulation data to accelerate physics evaluation by orders of magnitude, wrapped with conformal prediction for certified error bounds. For the rendering pipeline, I'm planning physically-based spectral path tracing in the style of Maxwell renderers for unbiased light transport, sphere tracing for SDFs to avoid meshing, and ray tracing NURBS through Bézier clipping or rational patch conversion. I'll use binned SAH for BVH construction with parallel optimization, packet and stream tracing with SIMD, and wavefront architecture for many-core execution. Multi-fidelity Bayesian optimization will combine coarse and fine simulation results. On the scientific visualization side, I'm adding volume rendering for fields, dual contouring for isosurfaces, line integral convolution for flow visualization, and tensor glyphs like stress ellipsoids. Differentiable rendering will serve as an optimization tool for inverse problems—matching target appearances or using photographs as design constraints. I could push toward spectral rendering with polarization using Mueller calculus, though I'll keep it within plausible bounds. For the concurrency layer, I'm building on asupersync—the user's async runtime with structured concurrency and cancellation semantics. Each kernel task becomes a cancellable scope, and simulation steps are checkpo[int] Optimizer cancellation becomes load-bearing here: when sequential testing identifies a dominated candidate, we kill it early, and asupersync ensures that cancellation is safe and responsive. For parallelism, I'm either integrating a work-stealing pool like rayon with asupersync or building our own, with careful attention to task granularity, NUMA topology on Threadripper (first-touch allocation, per-CCD pools, pinning), and Apple Silicon's P/E core asymmetry and unified memory model. For SIMD, I'm weighing portable SIMD via std::simd (still nightly) against unsafe intrinsic paths in core::arch, with multiversioning via function pointers resolved at startup to target AVX-512 on Zen 4/5 or NEON on ARM—keeping unsafe code confined to audited micro-kernels behind safe APIs where possible. On the memory side, I'm planning arena and bu[mp] allocators scoped to tasks, structure-of-arrays layouts, Morton-order curves for spatial locality, [an]d cache-oblivious algorithms for large transforms, plus huge pages on Linux via THP. For determinism across runs and core counts, I need fixed reduction [cut off] ——— Jeffrey Emanuel ✔ @doodlestein · 1h I seriously doubt that anyone out there could even understand every term mentioned in this entire thinking trace in a very deep way. There simply aren't enough years, and PhD programs are too specialized, and eventually humans have to DO something and earn a living: [Embedded thinking-trace screenshot, continuing, faded top line: "On the memory side, I'm planning arena and bump allocators scoped to tasks, structure-of-arrays layouts, Morton-order curves for spatial locality, and cache-oblivious algorithms for large transforms, plus huge pages on Linux via THP."] For determinism across runs and core counts, I need fixed reduction trees with bitwise reproducibility, Kahan summation for numerical stability, and optional stochastic rounding for research. Rather than relying on BLAS, I'm writing custom GEMM micro-kernels following the BLIS design—blocking for cache levels with batched small-matrix operations since the element matrices are tiny. For sparse operations, I'm supporting CSR, blocked CSR, and sliced ELLPACK formats optimized for SIMD. On persistence, I'm building a content-addressed store using BLAKE3 hashing to track all geometry and field artifacts, with experiment tracking that records the full lineage of optimization runs so every design is perfectly reproducible and resumable. The database acts as a lab notebook with write-ahead logging for crash safety. For the API layer, I'm designing a capability-based interface where everything is a typed resource. The command algebra lets agents compose workflows as s-expressions or JSON that can be replayed and diffed, rather than making isolated endpoint calls. I'm also thinking through contracts with pre/postconditions for design-by-contract validation, plus making determinism, seeds, and resource budgets explicit parameters in the API. There's also some esoteric geometry—conformal and projective—that might ground the math. Now I'm layering in Clifford algebras and exterior calculus for the geometric primitives, cellular sheaves to handle interface consistency across distributed representations, and e-processes with optional stopping for anytime-valid inference in stochastic simulations. Optimal transport via Wasserstein metrics could help with surrogate certification and design racing. Shape calculus and Riemannian geometry of shape spaces give me the tools for design-space geodesics and topology optimization. Tropical geometry is particularly interesting here—max-plus algebra captures ReLU network behavior exactly and models task scheduling through the critical path, so I can use it to analyze the task DAG timing directly. For topology control, persistent homology lets me detect features like vortices and load paths in fields, and I can enforce topological constraints by controlling Betti numbers—ensuring a bracket has exactly the right number of holes. This ties naturally into Morse–Smale complexes for extracting structural skeletons. For robustness, interval arithmetic and Taylor models give me certified root finding through operators like Krawczyk and interval Newton. I'm also layering in spectral methods with Chebyshev expansions that automatically adapt their degree, treating functions as first-class objects for near-machine-precision calculus on profiles and boundary conditions. For large-scale problems, the fast multipole method and hierarchical matrices accelerate boundary element solvers for exterior aerodynamics and elasticity. Time integration uses Lie group and symplectic integrators to preserve the underlying physics structure—exactly conserving discrete momenta and maintaining good energy stability over long simulations, especially for rigid body dynamics on SE(3). The optimizer suite itself sits within an information-geometric framework where natural evolution strategies emerge as natural gradient descent on the Gaussian family. Randomized numerical linear algebra—sketching, Nyström approximation, and trace estimation via Hutchinson sampling—provides modern, scalable tools for sensitivity analysis and preconditioning in topology optimization. Reduced-order models built from Koopman operator theory and POD-Galerkin projection with DEIM nonlinearity handling let the optimization loop run 100–1000x faster by working in a compressed space rather than the full high-dimensional system. Beyond that, compressed sensing reconstructs fields from sparse probe data, while graph-based methods handle mesh partitioning (multilevel spectral refinement like METIS) and design graphs for lattice infill generation. The ground structure method—where a huge graph of potential truss members gets pruned by the optimizer—connects beautifully to Michell truss theory and continuum-limit layout optimization formulated as linear or semidefinite programs. For the solver side, I'm thinking about implementing a modern first-order conic solver in Rust (ADMM or PDHG variants) to handle the SDP and SOCP subproblems that arise from moment relaxations and sums-of-squares hierarchies. The Lasserre hierarchy is particularly elegant here—it gives certified global optima for polynomial optimization problems through SOS certificates, which is powerful for certifying optimality on low-dimensional shape parameters. I'm also considering applying SOS Lyapunov functions to flight dynamics stability, where I'd linearize the trim conditions and use SOS to certify the re[gi]on of attraction—directly connecting to the stability and maneuverability objectives. Beyo[nd th]at, there's the question of whether to extend beyond conformal geometric algebra into spinor representations, though keeping CGA/PGA might be [cut off] ```