Case Study: Multi-Modal Physics Discovery
Re=1000 • Strategic Plasticity • Pareto Frontier Exploration
Objective: ATOM demonstrated the capacity for Strategic Plasticity. Across three independent training runs, the system discovered three distinct, valid physical mechanisms to solve the same active flow control challenge.
The Challenge Environment
In this phase, ATOM was moved from simple 2D benchmarks to a constrained 3D physics environment. The "Body" (cylinder) was fixed, creating a downstream wake of alternating high-low pressure zones (The Von Kármán Vortex Street). The "Brain" explored the solution space to find optimal damping strategies.
Transitional regime.
49,152 voxels (Hardware Limits).
Trailing-edge pulsing.
Performance Audit: The Pareto Frontier
Unlike standard DRL which converges to a single local minimum, ATOM explored a Pareto Frontier of control strategies, trading off between stability, energy efficiency, and prevention.
| Key Performance Indicator (KPI) | Value | Interpretation |
|---|---|---|
| Lift Variance Reduction | 38.5% – 41.3% | Dependent on selected strategy (Linear vs. Inverse). |
| Neural Stress (Final) | 0.0000* | *Subject to Neuro-Symbolic clamping in high-gain regimes. |
| Convergence Rate | 100 Steps | 99.99% faster than traditional RL ($10^6$ steps). |
Discovery Audit: The Three Laws of Atom
The most critical achievement was not a single equation, but the evolution of physical reasoning across three runs. ATOM moved from reactive damping to preventative pressure maintenance.
Strategy A: The "Reactive" Linear Law (Run 1)
Mechanism: Proportional (P) Control. ATOM focuses on the shear layer, waiting for a vortex to detach before canceling it. (38.5% Reduction)
Strategy B: The "Efficient" Quadratic Law (Run 2)
Mechanism: Energy Suppression & Gain Scheduling. ATOM recognized that drag scales with $v^2$. It autonomously derived a gain scheduler to lower control authority when flow slows down. (39.5% Reduction)
Strategy C: The "Preventative" Inverse Law (Run 3)
Mechanism: High-Gain Pressure Maintenance
(Singularity Control). ATOM shifted focus to the recirculation bubble. By monitoring
Latent_2 (Base Pressure), it detects precursors of shedding and applies asymptotic
force to prevent pressure collapse. (41.3%
Reduction)
Contextual Strategy Switching
Visual forensics confirm the strategy switch. In Run 1, ATOM looked at the Shear Layer (Symptom). In Run 3, it shifted attention to the Recirculation Bubble (Cause).
Academic Validation
14.2 Comparison with Published Results
| Study | Method | Reduction | Reynolds No. |
|---|---|---|---|
| Rabault et al. (2019) | DRL (PPO) | ~8% Drag | Re=100 |
| ATOM (Run 1) | Phase-Matched Blowing | 38.5% Lift Var | Re=1000 |
| ATOM (Run 3) | Adaptive (Inverse Law) | 41.3% Lift Var | Re=1000 |
14.3 What ATOM Discovered
- Location Matters: The saliency map focused on the trailing edge shear layers (Run 1) and then the recirculation bubble (Run 3).
- Mechanism Matters: The system autonomously switched from Kinetic Energy Damping ($v^2$) to Pressure Maintenance ($1/P$).
- Safety Matters: The system discovered a high-performance singularity ($1/x$) that requires Neuro-Symbolic clamping to be safe.
Final Verdict
ATOM is not just optimizing numbers; it is Reasoning about Fluid Mechanics. It moved from reacting to vortices to starving them of energy, and finally to preventing them entirely.
This is a Hypothesis Generation Engine for Physics.