ABOUT THE FOUNDER
Kyle Clouthier
From computational physics research to breakthrough algorithms. Building three core technologies: exact arithmetic, wildfire prediction, and tail-risk quantification.
The Story
It started with a deep fascination for computational physics. I spent years immersed in quantum simulations, fluid dynamics, and numerical methods — writing custom physics code and pushing hardware to its limits. That work ignited a passion for understanding how computers model the physical world.
But every computational scientist eventually hits the same wall: floating-point drift. GPUs are fast, but every operation introduces tiny rounding errors. Run enough operations and those errors compound. Your simulation diverges. Your results become unreliable.
# Standard GPU arithmetic
(1016 + 1) - 1016 = 0 # WRONG
# The answer should be
1
So I built VLA — 512-bit exact arithmetic at GPU speed.
Then I applied it to a real problem: wildfire prediction. Existing models either need months of manual calibration or fail catastrophically on extreme fires. The 2024 Jasper Fire burned 36,000 hectares — standard physics models predicted 6× less.
So I built SimFire — physics-ML hybrid trained on 500+ real fires.
Working on wildfire risk revealed another gap: standard actuarial models assume normal distributions. But wildfires follow heavy-tailed distributions — extreme events happen far more often than bell curves predict. Insurers are systematically undercapitalized.
So I built Mixture LDT — heavy-tailed risk quantification that captures what standard models miss.
My background spans Python, CUDA, C++, TypeScript, and PyTorch. I've built everything from low-level GPU kernels to differentiable physics models to full-stack web applications. This cross-disciplinary expertise — combining deep mathematical foundations with practical engineering — is what makes SimGen possible.
What I Built
VLA — Verified Lossless Arithmetic
110+ custom CUDA kernels providing 512-bit exact arithmetic with zero accumulation error.
4.4M×
faster than mpmath
0
accumulation error
512-bit
integer precision
100%
cross-GPU identical
SimFire — Wildfire Spread Prediction
Differentiable physics-ML model combining Rothermel/CFFDRS equations with neural region encoders. Deploys globally without fuel maps or manual calibration.
500+
real fires trained
7×
more accurate (Jasper)
0
fuel maps required
Global
deployment ready
Mixture LDT — Tail Risk Quantification
Heavy-tailed distribution modeling combining normal years with catastrophe years via Pareto tails. Captures extreme events that standard actuarial models systematically miss.
1027×
standard underestimate
37%
undercapitalized
Pareto
tail modeling
Bimodal
distribution fit
Three technologies. Each solves a class of problems that standard tools cannot.
Proven Results
Jasper 2024: 7× More Accurate
SimFire predicted 20.1% burn area (actual: 21.2%). Cell2Fire physics model predicted 5.8% — a 6× underestimate on the same fire.
Insurers 37% Undercapitalized
Mixture LDT reveals standard actuarial models miss extreme wildfire tail risk. Standard models underestimate by 1027× at distribution tails.
NumPy Gets Math Wrong
Hilbert matrix determinant: NumPy returns WRONG SIGN at n=14,15,16,17,20. VLA returns correct positive value.
4.4 Million × Faster
VLA vs mpmath arbitrary precision: 183 seconds → 0.00004 seconds for 512×512 exact matrix sum.
Why Work With Me
I'm not a sales team. I'm not a faceless corporation. I'm the person who built these technologies and I'm the person you'll work with directly.
Whether you need fire prediction, tail-risk analysis, or exact computation — you need someone who understands the math, wrote the code, and can customize solutions for your domain.
What I bring:
- Deep expertise in numerical methods, differentiable physics, and risk modeling
- Built all three systems from scratch — 110+ CUDA kernels, physics-ML models, statistical engines
- Direct access — no account managers, no support tickets
Let's Talk
Free discovery call. Tell me your hardest problem — whether it's fire prediction, tail risk, or numerical precision.