Flow viz
[ SciPhy · v0.3 · autonomous simulation ]

You design.We predict.

— keep building —

Engineering has spent too long waiting on siloed expertise, disconnected tools, and workflows that kill momentum. We bring performance insight into the design process, so engineers can move faster, make better decisions earlier, and stay focused on the work that matters most.

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Deeply Integrated Simulation Agent

SciPhy's platform and agents work collaboratively to automate your most complex simulation workflowsFrom DOE setup through post-processing, agents drive every step end-to-end — leaving you with answers, not job scripts.. We handle the computational heavy lifting so your engineers can focus on design, rapid iteration, and extracting real engineering insight.

Fig. 2.0 engineer effort by autonomy tier n = 1,240 studies

SciPhy Platform.

Enable a new era of autonomous engineering, from design to analysis, so you can focus on insights and impact.

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diminishing returns 1 48r · P0.62 3 90r · P0.93 · recommended best confidence per run 2 320r · P0.98 0 100 200 300 ITERATIONS · RUNS P(pareto-hit) ↑ FIG 2.1.0 Candidate DOEs · pressure-ratio study
Maximize pressure ratio, hold η ≥ 90%. YOU
Here are 3 candidate experiments — I'd run adaptive. 1trailing-edge 2full-factorial 3adaptive
§2.1 · DOE

State the mission
not the mechanics.

State an objective. SciPhy proposes a few ways to reach it — parameter sweeps, full-factorial studies, adaptive refinement — weighs each on runtime versus confidence, and runs your pick in parallel across your compute. You set the goal; it plans the path.

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geom_02 · δ=3.8° 2.6 3.8 4.2 5.3 6.0 TRAILING-EDGE Δ-ANGLE · ° → 0.92 0.88 0.84 0.80 0.76 STAGGER β · NORM ↓ FIG 2.2.0 Latin Hypercube5 Geometries Spawned
§2.2 · Optimization

Search a vast design space.

Sweep parameters and vary geometry automatically, with proven optimization methods steering toward the best design. Campaigns that take days to set up by hand come together in a sentence.

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RUN
SPAWN_ID
EFF.
MASS FLOW RATE
Δ
#1847
a7f3c2
0.871
406 lbm/s
+0.04
#1212
8e1b9d
0.864
436 lbm/s
−0.02
#1106
4c2f81
0.852
388 lbm/s
+0.01
#0974
b6d4a0
0.844
452 lbm/s
−0.06
#0813
3a8e1c
0.829
370 lbm/s
−0.11
#0612
9f5b2d
0.868
417 lbm/s
+0.03
§2.3 · Data

Let prior work shape your next design.

Every run, geometry, mesh, and result is available for easy retrieval. Compare campaigns months apart in a single prompt — and never guess what you may have already done.

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η → 0.871 ITERATION # EFFICIENCY · η ṁ → 406 lbm/s ITERATION # MASS FLOW RATE (lbm/s) FIG 2.4.0 convergence · normalized on iteration count
§2.4 · Analysis

From thoughts to plots.

Post-processing and data plotting alone demand standalone tools and deep expertise. Just describe it — plot, analyze, and compare in one place.

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§2.5 · Pre/Post

Agent-native 3D post-processing.

Geometry import, mesh adaptation, slicing, contour plots, isosurfaces, vector fields — all native, all in 3D, all orchestrated by the agent and ready to share.

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SOLVER · 14h / run SURROGATE · 0.05s / run DESIGN SPACE → ↑ ACCURACY FIG 2.6.0
1000× speedup
§2.6 · Solvers

Choose how to predict the physics.

Run industry-grade, GPU-accelerated solvers on your hardest cases — automate model training and model rollout for rapid use.

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run #1847 · pareto-dominant η 0.871 · ṁ 406 lbm/s anomaly · #1212 shock onset M=0.84 0.80 0.82 0.84 0.86 0.88 0.90 EFFICIENCY · η → 150 175 200 225 250 MASS FLOW RATE (lbm/s) FIG 2.7.0 pareto front · 1,847 runs
§2.7 · Insight

Chase the insight not the data.

The agent reasons across runs — flagging anomalies, identifying Pareto-dominant designs, and recommending the next move. Engineering judgment, not just dashboards.

§04 · Contact

Talk to our team.

Our team spent 30 years building some of the world's most advanced aerospace, automotive, and industrial systems. Everywhere we looked, physics simulation held real answers, but only a handful of specialists could unlock them. SciPhy tears down that barrier, so every engineer can get performance insight without wrangling the tools — and get back to building.

Experienced team
Pratt & Whitney Stanford University Boom Supersonic Georgia Tech Ford Motor Co Solar Turbines · Caterpillar Virginia Tech