Autonomous Scientific Discovery Agent

Project ATOM • v0.1.0 Alpha • Research Preview

Neuro-Symbolic
Control Architecture

ATOM-OSS-SDK-RP1 Out Now!
ATOM stabilizes chaotic regimes ($Re \ge 1000$) by enforcing Symplectic Energy Constraints and gating neural intuition with Symbolic Law Discovery.

TERMINAL_OUTPUT :: ATOM// WORLD: LBM SOLVER (Low-Dimensional Proxy) LIVE
> HYPOTHESIS DETECTED:
> turbulence*(-3.5467212)
> CONFIDENCE: 99.8%
Request Pilot ATOM-OSS-SDK 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

Constraint-Based Reasoning.

We don't just "train" on physics; we embed it.
By enforcing Divergence-Free Fields ($\nabla \cdot \mathbf{v} = 0$) and Hamiltonian Conservation ($\frac{dH}{dt} = 0$), ATOM guarantees physical validity where Generative AI fails.

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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. THE TRUST GATE TOPOLOGY
Liquid Net
Plasticity (1 - α)
Symbolic Law
Rigor (α)

$State = (1 - \alpha)\Psi_{NN} + \alpha \Psi_{Symb}$

Equation 3.1 from core/brain.py

Actuator Output
α=0.8
α

The Trust Gate

Active Inference Mechanism

Pure Neural Networks are fast but hallucinate. Pure Physics Solvers are rigorous but slow.

ATOM uses a Trust Gate ($\alpha$) to dynamically blend them. When the Symbolic Scientist discovers a valid law (high confidence), $\alpha \to 1$, forcing the neural state to collapse onto the physical manifold.

Step 0-100: Exploration

$\alpha \approx 0.0$. The agent runs on pure neural intuition to explore the state space.

Step 400: Discovery

> HYPOTHESIS: $R \approx 20 - 1/v$
> CONFIDENCE: 45%

Step 800: Manifold Lock

$\alpha \to 0.98$. The system locks onto the inverse law ($1/P_{base}$), effectively becoming a PID controller derived from scratch.

Beyond Acceleration.
Autonomous Reasoning*.

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

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

*Benchmarks on Consumer Hardware, Ready to scale

10-13
Energy Drift
(Symplectic Regularisation)
3
Scientific Hypotheses
(Discovered Autonomously)
Neural-Symbolic Atom researching Spectral Navier-Stokes
Discovering Law: Strategy C
10-13
Hamiltonian Drift
41.3%
SOTA Lift Reduction
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.

Setup: Re=1000 | Grid: 64x32x24

Ablation Study (Control vs. Experiment)

Method Regime Result
Clean RL Baseline Re=100 Gradient Collapse FAIL
Rabault et al. (2019) Re=100 ~8% Drag Reduction
ATOM (Symplectic Gate)
Re=100 41.3% Lift Stabilization
ℹ

Why this matters: Standard Deep RL fails to generalize in chaotic regimes ($Re \ge 100$) due to non-stationary dynamics. ATOM succeeds by enforcing Symplectic Energy Constraints ($dH/dt \approx 0$).

Iterations: 120 (vs 10,000+ traditional)
3D Cylinder vortex shedding
Read Full Case Study →
Phase-Matched Blowing | Re = 100 STEP: 120
Cylinder Flow Control Visualization
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, Neuro-Symbolic Scientific Intelligence."

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

Standard Black-Box policies learn 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 (v0.1.0)
Human and AI collaboration at the blackboard

Domain Transfer Validation

From Aerodynamics to
Analog Gravity

Proof of architecture: We swapped the solver kernel from D3Q27 (Fluids) to D2Q25 (Acoustics). The Neuro-Symbolic Agent adapted instantly, stabilizing a supersonic horizon without retraining the reasoning core.

Acoustic Hawking Radiation Simulation
Mach 1.2 Supersonic Sink

The "Black Hole" Test

Lattice Topology D2Q25 (4th-Order Hermite)
Flow Regime Supersonic (Mach 1.2)
Phenomenon Captured Acoustic Hawking Radiation

"The acoustic black hole demo uses the exact same ATOM architecture that solves automotive CFD. This confirms Universal Differential Reasoning across discrete physics domains."

Public Beta Alpha 0.1.0

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)

JAX implementation of Hodge-Net Layers. Project your gradients onto the solenoidal manifold with a single decorator.

LBM Skeletons

A library of differentiable D2Q9, D2Q25, 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 shape optimization.

REPOSITORY: ATOM-OSS-SDK VISIBILITY: Research Preview Available Now
> git clone github.com/culturiqai/atom-oss-sdk
Initialize Physical Manifold... [DONE]
Loading Helmholtz Decoder... [READY]
# 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
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
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