Optimizing Complex Mathematics for Hardware Execution
Full Metal Analytics is a deep-tech startup specializing in the design and validation of hardware-optimized numerical kernels. We bridge the gap between complex mathematical theory and silicon-ready implementations.
Founded by PhDs in Applied Mathematics, we are currently validating high-performance algorithms through resource-constrained software prototyping to ensure optimal efficiency for future hardware integration.
Our Development Path
Algorithmic Discovery
Deriving rigorous mathematical models for complex dynamics.
Resource-Constrained Validation
Prototyping via web technologies and low-level software to profile performance.
Hardware Integration
Transitioning validated IP into specialized silicon and EDA flows.
Numerical IP Design
Creating stable, high-precision kernels designed for minimal silicon footprint and maximum throughput.
Hardware-Software Co-Design
Bridging the gap between mathematical complexity and low-level hardware constraints.
Performance Profiling
Rigorous benchmarking of algorithmic efficiency in resource-constrained environments.
Targeted Computational Domains
Validation Prototypes
Current research focus: testing mathematical stability and computational efficiency in resource-constrained environments.
Quant-Kernel Validation
Prototyping pricing algorithms for low-latency environments.
Gradient Engine Prototyping
Testing automatic differentiation for hardware acceleration.
Development Progress
AAD Kernel Validation
25 Aug 2025Benchmarking alpha-release of our adjoint engine.
Web-Based Prototyping
29 Jul 2025Successful implementation of WASM-based PDE solvers for rapid algorithmic prototyping.
Products
Risk Management Live
A simple application for managing trading positions, calculating risk sensitivities and pricing financial instruments.
- Optimized numerical algorithms for fast and efficient calculations.
- Integrate your own mathematical models and financial instruments.
- Designed to collaborate seamlessly with AWS or on-premises cloud.
Automatic Differentiation Live
An educational application for generating automatic differentiation code.
- Generate tangent and adjoint code using Python-like syntax.
- Runs entirely client-side in the browser.
A simple application for managing trading positions, calculating risk sensitivities and pricing financial instruments.
- All-in-one browser application engineered for performance.
- Optimized numerical algorithms for fast calculations.
- Includes support for Black-Scholes, Dupire and Heston.
About
Full Metal Analytics is a partnership of PhDs in mathematics specializing in high-performance numerical methods.
We focus on the practical implementation of mathematical theory, specifically designing numerical kernels that are optimized for hardware execution. Our goal is to bridge the gap between complex mathematical models and the constraints of silicon-ready software and hardware.
Numerical Implementation
Our expertise includes the development of PDE solvers using adaptive mesh refinement and finite element methods. We focus on the numerical linear algebra required to make these solvers efficient, selecting algorithms and preconditioning strategies that balance computational accuracy with speed.
Hardware-Software Co-Design
By combining mathematical expertise with a background in low-level systems and electronics, we approach problems through a hardware-aware lens. We work to ensure that complex simulations can run in real-time on specialized hardware, providing both consulting and validated IP for demanding computational environments.
Technical Inquiries
Direct contact for consulting, licensing, or research collaboration.
Validation Prototypes
Real-time implementations demonstrating mathematical stability and computational efficiency within resource-constrained browser environments.
Powered by modern web technologies
Moving Mesh
2D PDE Solving
1D PDE Solving
Automatic Adjoint Differentiation
Responsive Mesh Dynamics
Mesh adaptation is a vital mathematical tool for solving complex, high-gradient problems by concentrating computational resources where they are needed most. This demo was developed by our team of numerical analysts to showcase our expertise in advanced PDE solving.
Presets
Mathematical Settings
Visual Styles
Advanced settings
Reaction-Diffusion: Adaptive Dynamics
Unlike traditional h or p-refinement, this demo utilizes an evolving moving mesh to concentrate resolution on high-gradient features. By optimizing the ratio of accuracy to degrees of freedom, we can achieve high-fidelity results with significantly fewer mesh points, carefully balancing mesh-update overhead against computational savings.
The Gray-Scott reaction-diffusion pattern generating model
Initial Conditions
Reaction Parameters
Mesh Settings
High-Speed 1D ADE Solver
This solver implements an Alternating-Direction-Explicit (ADE) scheme to achieve high-speed simulation of advection-diffusion-reaction dynamics. The lack of a global dependency (which occurs in implicit methods where every point depends on every other point through the matrix solve) allows for massive scaling across thousands of processors, enabling rapid, real-time parameter exploration.
Domain: t > 0, xleft ≤ x ≤ xright
∂u/∂t = A ∂²u/∂²x + B ∂u/∂x + Cu + D
Initial: u(t=0, x) = u0(x)
Left BC: α0u + β0u' + γ0u'' = ε0
Right BC: αNu + βNu' + γNu'' = εN
Automatic Adjoint Differentiation (AAD)
Automatic Adjoint Differentiation (AAD) provides a mathematically exact way to compute sensitivities (gradients) without the prohibitive cost of finite difference methods. By propagating derivatives backward through the computational graph, AAD enables high-dimensional optimization and parameter estimation with a computational cost that is nearly independent of the number of input variables, making it a critical technology for real-time risk management and hardware-accelerated machine learning.
Privacy Policy
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We may process and store information that you provide to us as part of normal business operations.
By using our website or services, you hereby consent to our Privacy Policy and agree to its terms.
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What we do with your data Any information provided to Full Metal Analytics will be processed by us and will not be shared with third parties without your explicit consent.
Version 3 - 16 Sep 2025