CETTE / Demo

ok_computer2.0

De la matière
à la découverte.

La pluie. La charge. La durée.

A pavement is a system.

How can polymer composition, grid geometry and installation work together to carry loads and manage stormwater?

01 / Inputs

Define the conditions.

Polymer blend and manufacturing history
Grid geometry, aggregate and pavement layers
Rainfall, initial moisture, sediment and drainage
Load history, temperature and installation conditions

02 / Model path

Connect the specialists.

CETTE-MIXPROC
Connect polymer ingredients and proportions to an ordered manufacturing route.
CETTE-MÉSOLIEN
Connect processing and material structure to effective mechanical and transport parameters.
CETTE-CHAMPOP
Map geometry, material parameters and boundary conditions to hydraulic and mechanical fields.

03 / Output quantities

Make the question measurable.

Ponding, stored water and outflow over time
Water balance across the pavement assembly
Load response, displacement and permanent deformation
Uncertainty for the stated material and operating conditions

04 / Verification

Compare hydraulic predictions with infiltration and drainage measurements, and mechanical predictions with repeated-load tests on the specified pavement assembly.

Les réseaux & les méthodes

18 components.
One scientific engine.

Seventeen neural model families and variants, plus the EMBEDQ quantum workflow. Each has a defined scientific role within version two.

ok_computer2.0 — the complete version-two portfolio
Component	What it does
CETTE-ÉQUIAT	Conservative equivariant atomistic learning ÉQUIAT learns how the local environments of atoms contribute to the energy of a material.
CETTE-ÉQUIAT-LR	Atomistics with explicit long-range interactions ÉQUIAT-LR extends the atomistic family to problems where interactions beyond a local geometric neighborhood matter.
CETTE-ÉQUIAT-MF	Method-conditioned atomistic predictions ÉQUIAT-MF connects calculations made under different, explicitly identified scientific methods.
CETTE-INVAR	Invariant property learning and baseline inference INVAR retains a direct route from a material representation to a defined property.
CETTE-COMPACT	Compression and efficient deployment COMPACT carries trained scientific capabilities into a smaller deployment model.
CETTE-SYMGÈNE	Conditional crystal generation SYMGÈNE explores compositions and periodic structures conditioned on useful properties and permitted chemistry.
CETTE-MIXPROC	Mixture and processing design MIXPROC searches the joint space of ingredients, proportions and manufacturing history.
CETTE-SYNTHÉVAL	Condition-aware synthesis feasibility SYNTHÉVAL evaluates how a proposed material could be prepared within a real precursor and equipment envelope.
CETTE-MÉSOLIEN	Microstructure and scale transfer MÉSOLIEN connects materials chemistry and preparation history to the microstructures that govern engineering response.
CETTE-CHAMPOP	Field prediction with neural operators CHAMPOP learns the map from a physical problem specification to spatial or time-dependent fields.
CETTE-CINÉTA	Constrained reaction and process dynamics CINÉTA combines learned kinetic information with explicit chemical and engineering structure.
CETTE-SUPRAC	Superconducting-material screening SUPRAC organizes superconductivity discovery around composition, structure, phase and measurement conditions.
CETTE-FLUXCIR	Superconducting-circuit design and response models FLUXCIR connects Coltrane circuit topology, device geometry and fabrication context to superconducting-circuit behavior.
CETTE-SYNDÉCODE	Temporal quantum-error decoding SYNDÉCODE uses the history of quantum-error syndromes to infer the information needed for decoding.
CETTE-ÉLECTROFLUX	Fuel-cell electrochemistry, transport and degradation ÉLECTROFLUX links fuel-cell materials to operation at the cell scale.
CETTE-ONDEQ	Variational neural electronic wavefunctions ONDEQ represents an electronic wavefunction with a neural model and optimizes it through variational Monte Carlo for a specified Hamiltonian.
CETTE-EMBEDQQuantum workflow	Quantum embedding and variational eigensolver research EMBEDQ places a selected electronic subproblem inside an explicit embedding environment.
CETTE-NEUROCIR	Neural accelerator and hardware/software co-design NEUROCIR connects the structure of an AI workload to accelerator architecture, software mapping and physical implementation.