The Engineering Lab

AI-Native
Engineering
Simulation.

Build, train and deploy physics-aware models from engineering data. Solver-agnostic. Physics-consistent. Validated.

Explore Studio → Read Research
Observe.
Ingest geometry & data
Model.
Train under constraints
Predict.
Validated physics
[ FIG.01 ] Physics-aware field
Solver-agnostic
engine
// Manifesto

Engineering software was built for CPUs. Agnostix is being built for AI.

Engineers should spend less time configuring solvers and more time exploring solutions. The bottleneck in mechanical engineering was never the physics — it was the friction around it.

By combining simulation, machine learning and physical constraints, we enable a new generation of engineering systems: ones that learn the governing laws, respect them, and explain their answers.

Observe

Ingest geometry, materials, boundary conditions and sparse sensor data.

Model

Physics-informed models trained under known constraints.

Predict

Validated fields, stresses, reaction forces — with a report.

01The ProblemExpert-Only CAE
Engineering simulation is still trapped in expert-only CAE workflows.

Traditional CAE demands deep specialist knowledge, heavy manual setup, expensive licences and long waits before a trustworthy result. Every iteration means re-meshing and re-configuring solvers.

Agnostix proposes an AI-native layer that lowers the barrier: engineering intent goes in, structured simulation comes out — validated, reproducible and explainable.

// Legacy CAE: expert → setup → mesh → solve → re-mesh → repeat.
// Agnostix: intent → model → validated result.

02Agnostix AssistantEngineering Reasoning at Scale
Input — Natural Language
“Create a thermoelastic study.”
→
Output — Executable Study
PhysicsLinear Elasticity
FormulationDisplacement
MaterialsSteel · Aluminum
BoundaryFixed · Load · Thermal
EnginePINN Standard v2
OutputField · Stress · Report
03The Platform06 Modules
M-01

Studio

Build engineering intelligence.

The visual editor — the Figma of engineering. CAD, geometry, materials and boundary conditions in; model, study and training job out.
M-02

Train

Train physics-aware models.

The computational core. Solver-agnostic by design.

PINNsDeepONetFNOHybrid
M-03

Scholar

Research, reproduced.

A public library of reproduced papers, benchmarks and scientific datasets — every method validated in the open.
M-04

Assistant

Engineering reasoning at scale.

Describe a problem in natural language. Receive a structured, executable study.
M-05
Soon

Hub

Models. Datasets. Workflows.

A marketplace for reusable models, datasets and engineering workflows.

M-06
Soon

Deploy

From simulation to production.

Inference at the edge and in the cloud. Digital twins in the loop.

04Agnostix Train — The EngineSolver-Agnostic
// The user buys the result — not the method. Pick the engine that fits the physics.
PINNs
Physics-informed networks
Live
DeepONet
Operator learning
In Lab
FNO
Fourier neural operators
In Lab
Hybrid
Classical + learned
Roadmap
Under the hood — the PINN loss
Ltotal = λdata·Ldata + λPDE·LPDE + λBC·LBC
LdataFits sparse observations — learn from a handful of sensors, not a dataset.
LPDE = ‖N[u] − f‖²Forces the model to satisfy the governing physics N[u] = f everywhere.
LBCEnforces boundary & initial conditions across the domain ∂Ω.
Input Layer

Coords
& Time

  • x, y, z — spatial
  • t — temporal
  • continuous, mesh-free
Hidden Layers

5–8 Deep
50–200 Wide

  • sinusoidal / tanh activations
  • dense connections
  • smooth field representation
Output Layer

Solution
Field u

  • displacement
  • stress
  • temperature
AUTODIFFAutomatic differentiation computes spatial & temporal derivatives exactly — feeding the PDE residual with machine-precision gradients, no finite-difference approximation.
05Agnostix Scholar — Research, Reproduced05 Papers In Progress
Paper #001

Thermoelastic
PINN

Linear elasticity · displacement
0.18%Relative
error
Paper #002

Heat
Transfer

Steady-state · conduction
In
reviewReproduction
underway
Paper #003

Structural
Dynamics

Modal · vibration
In
reviewReproduction
underway
// Five papers being reproduced in the open. Each method benchmarked against high-fidelity references before it ships.

The Evidence

// Physics-aware models vs. classical Finite Element references
0×
Faster than FEM
at comparable accuracy
0
Relative error
thermoelastic PINN
0 hrs
Meshing time
required to solve
◆ Example Project 01

Rotor Bearing Bracket

A thermoelastic study of a rotor-bearing support — parts, materials, boundary conditions and results, structured in the .agnx engineering format.

Open Example →
06Who Builds ThisThe Lab
Founder of Agnostix in front of the lab wall
[ Founder · The Lab ]
“Engineers shouldn't start by choosing a solver. They should start with the problem.”
Agnostix
Built by a founder with a rare combination of mechanical engineering, software, automation / RPA and agentic systems — applying the discipline of operational workflows to computational mechanics.

[ Physics-aware. Solver-agnostic. Built for engineers. ]
07FAQCommon Questions
What is Agnostix, in one line?+
An AI-native engineering simulation platform that turns natural-language engineering intent into structured, reproducible and physics-aware simulations — with reports and optimization loops.
Why "solver-agnostic"?+
Engineers buy the result, not the method. Agnostix Train can run PINNs, DeepONet, FNO or hybrid classical-plus-learned models. You shouldn't have to choose the technique before you describe the problem.intent → engine selection → validated result
So where do PINNs fit?+
PINNs are our first live engine — physics-informed models trained under known constraints, mesh-free, learning from sparse data. They remain a technical advantage, but they are the engine, not the product.
How is the work validated?+
Every method is reproduced in the open through Agnostix Scholar and benchmarked against high-fidelity references before it ships. Paper #001, a thermoelastic PINN, reproduces at 0.18% relative error.
What is the .agnx format?+
A portable engineering-workflow file carrying parts, materials, boundary conditions, studies, chosen engine, parameters and results — making simulations reproducible across solvers and pipelines.

Explore
Studio

// We are onboarding research labs and R&D teams in waves. Drop your email for early access.

No spam. Physics only. ◆ agnostix.io