Sub-second predictive physics.
Accelerate complex computational fluid dynamics and finite element analysis. SimuDot bypasses traditional solver bottlenecks using deep learning models trained on historical physics data.
Physics solvers accelerated by AI.
By training deep neural networks directly on high-fidelity solver data, SimuDot delivers real-time FEA and CFD results with 99% solver accuracy, bypassing traditional mesh-generation bottlenecks.
Mesh-free predictive workflows.
Bypass traditional computational bottlenecks. Our platform ingests raw geometric CAD data directly, mapping boundary conditions to neural surrogates without manual mesh generation.
Distributed GPU scaling.
Scale massive computational workloads across distributed GPU clusters. SimuDot parallelizes neural inference, ensuring sub-second convergence even for complex, multi-million cell engineering models.
Three steps to convergence.
Ingest CAD geometry
Evaluate surrogate models
Verify solver accuracy
Upload raw step files directly. Our platform interprets boundary conditions and physical constraints automatically.
Deep neural networks predict stress fields and velocity vectors instantly, bypassing iterative mathematical loops.
Cross-reference results with traditional physics solvers to guarantee 99% accuracy before exporting high-fidelity reports.