Foundation physics modelsat the speed of software
Dynovia trains AI solvers on high-fidelity simulation so engineering teams can explore thousands of crash and thermal scenarios in the time one finite-element run used to take.
One model family for the full engineering loop
Design exploration
Sweep geometry, material, and load-case parameters interactively. Every variant returns a full field solution in seconds, not overnight.
Virtual certification
Run thousands of regulated load cases — FMVSS, Euro NCAP, JEDEC thermal cycles — and surface the failures worth putting on a physical rig.
Synthetic physics data
Turn a handful of validated solver runs into a labeled corpus that trains, fine-tunes, and stress-tests your own downstream models.
See how Dynovia accelerates your domain
Purpose-built model families for the physics that gates your release schedule.
Frontal rigid barrier · 56 km/h · deformation fieldPredict structural deformation, crash pulse, and occupant injury metrics across the full load-case matrix — before a single body-in-white exists.
- Full-vehicle and sub-structure deformation fields
- HIC, chest deflection, and intrusion metrics per case
- Frontal, offset-deformable-barrier, side pole, and rollover
Author load cases locally,solve them in the cloud
- 01Describe a load-case matrix in Python from your own environment — no GPU, no solver license, no meshing session.
- 02Dispatch thousands of variants to Dynovia Cloud and stream deformation fields, crash pulses, and injury metrics back as they finish.
- 03Wire the suite into CI so every geometry change is re-certified against the full regulatory matrix.
from dynovia import CrashStudy, LoadCase, sweepfrom dynovia.metrics import HIC15, intrusion @sweep(suite="bev-platform-c", variants=1024)def frontal_ncap(study: CrashStudy) -> None: body = study.load("cad/bodyinwhite_v7.step") body.material("front_rails", "DP980", gauge=sweep.uniform(1.2, 2.4)) case = LoadCase.rigid_barrier(speed_kph=56, overlap=1.0) result = study.run(body, case, horizon_ms=120) study.require(HIC15(result) < 700) # occupant study.require(intrusion(result, "toepan") < 90) # mm| Scenario | Runs | Pass | Status |
|---|---|---|---|
| frontal-rigid-56 | 256 | 98% | completed |
| odb-64-offset | 256 | 96% | completed |
| side-pole-32 | 192 | 93% | completed |
| small-overlap-64 | 192 | 89% | review |
| rear-impact-80 | 128 | 99% | completed |
Junction temperaturein the design loop
- 01Feed a floorplan and a power map; get a resolved die, package, and board temperature field in under a second.
- 02Sweep placement, TSV density, TIM selection, and cooling strategy interactively instead of waiting on overnight CFD.
- 03Transient models capture workload-driven hotspot migration, not just the steady-state worst case.
High-fidelity physics modules, off the shelf
Start from validated model families and material libraries, or fine-tune on your own solver data behind your firewall.
Solver cores
Explicit dynamics, conjugate heat transfer, electro-thermal, nonlinear statics, fatigue, multiphase.
Material models
Advanced high-strength steels, aluminium, composites, adhesives, mold compounds, TIMs, and interconnect stacks.
Geometry ingestion
STEP, CAD assemblies, GDSII floorplans, and existing solver decks converted to model-ready representations.
Validation suites
Reference solutions from LS-DYNA, Abaqus, and Icepak-class solvers with published error envelopes.
Validated against solver truth
Held-out geometries and load cases never seen during training, scored against production finite-element and CFD references.
| Load case | Reference solver | Scored metric | Error | Speed-up |
|---|---|---|---|---|
| Frontal rigid barrier · 56 km/h | Explicit FEA | Peak intrusion | 2.4% | 1,100× |
| Offset deformable barrier · 64 km/h | Explicit FEA | Crash pulse (OLC) | 2.9% | 940× |
| Side pole impact · 32 km/h | Explicit FEA | Door intrusion velocity | 3.4% | 1,260× |
| Mobile SoC · steady state | Conjugate heat transfer | Junction temperature | 0.4 °C | 2,300× |
| 3D-IC stack · transient burst | Conjugate heat transfer | Hotspot rise time | 1.8% | 1,800× |
| Power module · thermal cycling | Electro-thermal FEA | ΔT per cycle | 2.1% | 760× |
Held-out geometries and load cases · single A100-class GPU vs. 64-core CPU solver reference · full methodology available under NDA
Physics you can defend in review
Dynovia models are built to be audited. Every prediction ships with a calibrated uncertainty band, a provenance record of the training corpus, and a reference solver comparison — the evidence a safety case, a customer qualification, or a program review actually requires.
Conservation-aware architecture
Momentum, mass, and energy budgets are enforced as model constraints rather than hoped for, keeping rollouts stable far past the training horizon.
Calibrated uncertainty
Each field prediction carries a per-region confidence estimate, so engineers know exactly when to fall back to a full solver run.
Traceable provenance
Model cards record every solver version, mesh resolution, and material card that contributed to a given checkpoint.
Deploy where the data lives
Run in Dynovia Cloud, in your VPC, or fully air-gapped on premise for ITAR and export-controlled programs.
Why foundation physics is a national-competitiveness problem
Regulatory load-case coverage that is today rationed by solver cost becomes exhaustive, moving safety findings earlier in the program.
Thermal design closure at the speed of layout iteration shortens the path from architecture to tape-out for U.S. fabless and packaging teams.
Small suppliers without solver licences or HPC clusters get the same physics fidelity through a browser and a Python API.
Bring foundation physicsinto your engineering loop
Tell us the load case or thermal problem that gates your schedule. We will run it against our models and show you the comparison.
hello@dynovia.ai