Stop guessing at reality. Fabi Analytics engineers the GlassBox.AI Architecture™—replacing opaque, statistical black boxes with deterministic, continuous manifold dynamics designed for extreme physical environments.
Reversing the arrow of causality in computational physics. Standard molecular dynamics force pre-calculated energy fields onto rigid spatial grids to guess how molecules fold or bind. GlassBox TOPOS reverses this pipeline: by mapping the exact topological ground truth of a molecular network, the engine mathematically induces the bespoke, effective energy fields required to sustain it.
To bypass traditional quantum gridlock, TOPOS executes this inverse architecture across two mathematical domains:
The Energy Sweep (Static Equilibrium): TOPOS does not require physical movement to map the field of a static structure. Utilizing electrochemically weighted multifiltrations, the engine expands interaction energy thresholds across stationary physical coordinates, mapping the macroscopic field contour by reading the exact depth of potential energy wells and structural voids.
The Jacobian Response (Dynamic Ensembles): For systems in constant motion—like Intrinsically Disordered Proteins—TOPOS applies continuous volumetric shearing to deform the manifold. By tracking how topological invariants resist this physical strain, the engine calculates dynamic restoring forces.
Operating beyond simple 3D grids, this architecture captures real-time electrodynamic cooperation. It instantly reads the topological shockwave of shifting energy radii during chemical collisions. Anchored by strict thermodynamic constraints within an E(3)-equivariant Hamiltonian class, TIFM effectively solves the Inverse Force Field problem—engineered to predict complex phenomena from novel therapeutic docking to the self-assembly of silica microsphere lattices.
Standard artificial intelligence treats the physical world as a black box. Our stack is built upon a foundational Continuous Neural Operator (CNO), engineered from the ground up to respect physical laws, geometric structures, and continuous spatial mechanics.
Edge-AI sensor fusion focusing strictly on raw feed intelligence. ORE utilizes proprietary modules like DeepVISION for high-speed optical LiDAR particle size distribution (PSD) and synchronous microwave moisture tracking directly at the crushing circuits.
The state-transition engine for structural integrity. HEAP utilizes 3D poroelastic Finite-Difference Time-Domain (FDTD) lattices to map fluid saturation inside the pad, correlating piezometer telemetry to predict localized slumping risks in real-time.
Closed-loop chemical kinetics targeting reagent optimization. REACTOR continuously adjusts cyanide and lime titration dynamically against shifting geometallurgical baselines to mitigate unpredictable sulfide spikes and prevent gold lock-up.
Predictive operational safety and maintenance tracking. ASSET monitors sub-threshold variations across secondary crushers and HPGR circuits to mathematically isolate the signature of component fatigue weeks before catastrophic mechanical failure.
GlassBox VISION is a new paradigm image diagnostic engine. It utilizes a physics-informed architectural prior, bypassing standard Euclidean convolution that memorizes pixels, in favor of explicit topological mapping to isolate biological anomalies.
A second-order kinetic optimizer that demonstrated superior accuracy, remaining stable on stiff PDEs and chaotic spatiotemporal systems where standard momentum (AdamW, Lion) and quasi-Newton (L-BFGS) methods catastrophically failed.
Advanced lattice operators engineered to map the intrinsic manifold of crystalline structures, monotonic functions, and complex sub-grid interactions.
Specialized topological extraction for analyzing hollow, porous, and complex interior geometries where traditional Euclidean mapping collapses.
Physics-Informed Neural Networks (PINNs) that learn the continuous manifold of active biokinetics, thermodynamic diffusion, and fluid environments.
Distributed intelligence for autonomous aerial and terrestrial agents. SWARM utilizes continuous-state routing to fluidly navigate multi-agent arrays through physical bottlenecks. It features an active mechanical isolation architecture for cooperative payload transport, neutralizing kinetic shockwaves via destructive counter-torque, and integrates predictive telemetry to proactively adapt the array against supersonic perturbations.
We do not rely on generalized off-the-shelf models. Fabi Analytics designs continuous mathematical spaces and dimensional reduction techniques built for the edge.
Constructing continuous neural operators that inherently understand the physical boundaries, energy conservation, and N-dimensional fields of the data they process.
Advanced signal extraction and noise-subtraction pipelines utilizing continuous mathematical transforms and spectral decomposition.
Custom C-extension pipelines and binary frameworks built to deploy high-density physics computations locally and efficiently.
Fabi Analytics Inc. was founded by Louis Fabbi, a computational systems architect specializing in the intersection of continuous physics, pure mathematics, and machine learning engineering.
Moving beyond opaque statistical algorithms, the firm leverages manifold learning, geometric topology, and continuous operator theory to construct physics-informed AI architectures. Based in Calgary, Alberta, Fabi Analytics translates theoretical physical science, fluid dynamics, and non-linear mechanics into robust, edge-native computational pipelines.