The Physics Foundation Model

Project ATOM • v3.1 Beta • Research Preview

Neuro-Symbolic
Scientific Intelligence

One architecture that perceives physics, controls systems in real-time, and discovers governing laws autonomously — from automotive CFD to analog gravity.

TERMINAL_OUTPUT :: ATOM// WORLD: LBM SOLVER LIVE
> HYPOTHESIS DETECTED:
> turbulence*(-3.5467212)
> CONFIDENCE: 99.8%
Request Pilot Technical Paper Investor / Partner

Built On

JAX PyTorch NVIDIA Modulus Julia

Issue 01: The Compute Wall

Compute Wall Illustration

Simulation is too slow.

Navier-Stokes equations are computationally irreducible. Simulating a full car aerodynamics run at high-fidelity ($Re > 10^7$) takes weeks on CPU clusters. Engineering iteration crawls to a halt.

Issue 02: The Black Box

Black Box AI Illustration

Standard AI hallucinates.

Generative AI (GANs/Diffusion) can "draw" fluids, but they violate conservation laws. They create energy from nothing. In safety-critical domains (Nuclear, Aero), hallucination is fatal.

The Solution

Neuro-Symbolic Solution

Neuro-Symbolic Rigor.

ATOM combines the speed of Neural Networks with the rigor of Symbolic Logic. We embed Conservation of Mass directly into the architecture, guaranteeing physical validity at 16x speed.

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The Computational Anatomy

System Architecture v3.1

The Brain
The Eyes
The Mind
The World

"Determine the anatomy of the machine..."

Hover over the diagram to inspect source.

FIG 2. RAW MEMORY BUFFER
VELOCITY (v)
REWARD (r)
!! STAGNATION DETECTED Step 400: Reward Collapse
π

The Symbolic Scientist

System 2 Reasoning

Deep Learning is excellent at intuition but terrible at explanation. ATOM solves this by periodically "sleeping" to distill its experiences into compact, human-readable physical laws.

Step 100: Observation

"Reward crashes when velocity approaches zero."

Step 400: Hypothesis

Candidate: 20.007 - x1 / (x1^2)

Step 800: Law Discovered

$$R \approx 20 - \frac{1}{v}$$

Bernoulli's Principle (Stagnation Penalty)
VERIFIED

Beyond Acceleration.
Autonomous Reasoning*.

Traditional solvers (JAX Oracle) provide ground truth data. ATOM provides Physical Laws.

By enforcing Symplectic Manifolds, ATOM explores a multi-modal Pareto Frontier—autonomously evolving from Linear Damping to Inverse Singularity Control.

*Benchmarks on Consumer Hardware, Ready to scale

10-16
Energy Drift
(Hamiltonian Consistency)
3
Scientific Hypotheses
(Discovered Autonomously)
Neural-Symbolic Atom researching Spectral Navier-Stokes
Discovering Law: Strategy C
10-16
Hamiltonian Drift
41.3%
SOTA Lift Reduction
16×
Faster Than CFD
3
Laws Discovered

Atom's Sandbox (2D Preview).

Touch to interact • D2Q9 Lattice Boltzmann Solver • Running Locally (JavaScript)

> KERNEL: NEURO_SYMBOLIC_LBM
> STATE: INITIALIZING
> AVG_VELOCITY: 0.000
> RENDER: BLUEPRINT_V4
> FPS: ...

"The computer is just a tool. The discovery is human."
- Computational Anatomy Labs

Deployed Intelligence

"Research Showcase: Multi-Modal Physics Discovery"

In this showcase, ATOM demonstrated Strategic Plasticity. Instead of converging to a single solution, the agent independently discovered a Pareto Frontier of three distinct physical mechanisms—moving from reactive damping to preventative pressure maintenance.

Benchmark vs. Literature

Rabault et al. (2019) Re=100 ~8% drag reduction
AIP.org (2021) Re=180 30% reduction
ATOM (2025) Re=1000 41.3% lift var. reduction

Re=1000 is 10× harder than Re=100. Order of magnitude improvement.

Iterations: 100 (vs 10,000+ traditional)
3D Cylinder vortex shedding
Read Full Case Study →
Phase-Matched Blowing | Re = 1000 STEP: 100
Cylinder Flow Control Visualization
SOTA: Lift Variance Reduction (-41.3%)

Research Protocols

Frequently Asked Questions

Is this a Physics-Informed Neural Network (PINN)? +

Not exactly. Standard PINNs use soft-loss constraints ($\mathcal{L}_{\text{physics}}$) which can still be violated. ATOM uses Hard Constraints via coordinate transformation (Helmholtz Decomposition) and Symplectic Integrators. Our architecture cannot represent a state that violates mass conservation, even before training begins.

How does the Symbolic Scientist work? +

We use a modified Genetic Programming approach (PySR) guided by a Transformer policy. During the "Sleep Phase," the agent replays high-reward trajectories from its Ring Buffer and attempts to fit parsimonious differential equations to the latent dynamics. These equations are then verified against the Ground Truth solver.

What hardware is required? +

The Production Solver (Teacher) requires NVIDIA A100/H100 clusters for high-Reynolds turbulence ($Re > 10^6$). However, the Inference Agent (Student) is highly optimized and runs in real-time on consumer hardware (Apple M3/M4 or NVIDIA RTX 4090), enabling edge deployment.

The Mission

"To create Trustworthy, Foundational Model for Scientific Intelligence."

We are not building another chatbot. We are building a true partner in the process of scientific discovery itself.

Standard AI learns statistical correlations. ATOM learns physical causality. By combining differentiable physics with symbolic reasoning, the system doesn't just optimize rewards—it discovers the underlying laws that govern our reality.

From the Whitepaper (v3.1)
Human and AI collaboration at the blackboard

Why "LBM"?

Same Architecture. Different Physics. Real Results.

MODULE: INCOMPRESSIBLE_CFD STATUS: VALIDATED
Validated

Incompressible CFD

Vortex shedding control at Re=1000. 41.3% lift variance reduction.

Beachhead Market: Automotive/Aero
CFD Visualization
MODULE: ANALOG_GRAVITY STATUS: DEMONSTRATED
Generalisation Demonstrated

Analog Gravity

Supersonic sink flow (Mach 1.2) simulating acoustic black holes with spontaneous Hawking radiation.

Same architecture, lattice extension
Analog Gravity Visualization
MODULE: HIGH_RE_INDUSTRIAL STATUS: IN_DEV
In Development

High-Re Industrial

Scaling to Re > 10⁷ on H100 clusters for production automotive CFD.

Targeting: Full-car aerodynamics
Industrial Aerodynamics Visualization

The acoustic black hole demo uses the exact same ATOM architecture that solves automotive CFD.
That's not a tool. That's a foundation model.

Public Beta Alpha 0.1

The Open Frontier

Democratizing Physical AI. In Q2 2026, we are releasing the ATOM Research Toolkit—giving the community the tools to be at the frontier of Physical AI.

C-FNO (Hard-Constraint FNO)

Pure JAX implementation of Helmholtz-Fourier Layers. Project your gradients onto the solenoidal manifold with a single decorator.

LBM Skeletons

A library of differentiable D2Q9, D3Q19, and D3Q27 lattice kernels. Built for GPU-accelerated inverse design.

Inverse Design Solvers

Gradient-based optimization primitives pre-coupled with our Hamiltonian Integrators for aerodynamic shape optimization.

REPOSITORY: ATOM_KIT_V1 VISIBILITY: UPCOMING
> git clone github.com/culturiq/atom-kit
Initialize Physical Manifold... [DONE]
Loading Helmholtz Decoder... [READY]
WARNING: Alpha 0.1 release scheduled for April 2026

# Sample Usage:
from atom.layers import CFNO
model = CFNO(constraints="solenoidal", lattice="D3Q19")
optimized_shape = model.inverse_design(target_drag=0.22)
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Engagement Paths

Work With Us

01

Enterprise Pilot

8-week proof-of-concept on your geometry. Validate speedup and accuracy against your existing CFD pipeline.

  • • Your CAD geometry
  • • Benchmarked vs. your solver
  • • Full technical report
  • • $50-150K depending on scope
Request Pilot
02

Research Partnership

Joint research, co-publication, technology integration. For corporate R&D labs and academic institutions.

  • • Novel physics domains
  • • Co-authored publications
  • • Technology licensing
  • • Structured in-kind + IP share
Research Inquiry
03

Investment / M&A

Pre-seed funding, strategic investment, or acquisition discussions. For VCs and corporate development teams.

  • • Pre-seed round open
  • • Strategic partnership equity
  • • Acqui-hire discussions
  • • Full technical due diligence
Investor Deck