Neural ODEs: When Physics-Informed Learning Actually Works
When should you use a neural ODE versus a classical numerical solver? We benchmark both on stiff ODE systems and find the surprising answer.
Deep dives into simulation, numerical methods, AI architecture, and high-performance engineering from the team at Turing Intelligence.
A detailed analysis of the tradeoffs between FVM and FEM in production-grade computational fluid dynamics pipelines. We explore conservation properties, parallel scalability, and practical accuracy benchmarks on real industrial geometries.
Read Full ArticleWhen should you use a neural ODE versus a classical numerical solver? We benchmark both on stiff ODE systems and find the surprising answer.
How we achieved a 4× memory-bandwidth speedup on N-body particle simulations using cache-oblivious data layout strategies in modern C++.
A walkthrough of the physical model and implementation behind our FMCW radar simulator — used to generate ground-truth synthetic datasets for ADAS ML pipelines.
Practical guidance on choosing between fixed-step RK4 and adaptive solvers like DOPRI5 when you're working with stiff ODE systems in real-time control loops.
The architecture decisions behind TuringLab's entity-component-system design for simulating thousands of concurrent autonomous agents with deterministic replay.
How we scaled PINN training across 32 GPU nodes using custom gradient synchronisation strategies that respect the structure of the underlying PDEs.