AI without physics is fragile. Physics without AI is slow. Neodustria unifies both.
Neodustria’s thesis: hybrid, physics-integrated intelligence — validated by simulation, explained by design, governed by provenance — will define the next decade of industrial R&D.
Neural operators & PINNs with constraints and uncertainty.
From material micro-structure to full system behavior for Predictive Manufacturing.
Agents that plan experiments, Orchestrate simulations, and justify steps (Explainability).
3D understanding of CAD/mesh/hypermesh for design intelligence.
Domain adaptation, calibration, and error bounds.
Model cards, lineage graphs, approvals, and audit evidence.
Constrained PDE learning with stability guarantees. We embed physics constraints and uncertainty calibration to ensure reliable behavior across regimes.
Knowledge graphs linking CAD, BOM, process, and markets. Ontologies harmonize Engineering Intelligence semantics across design, simulation, and supply data.
We train Physics-Aware Surrogates for thermodynamics, aerodynamics, ergonomics, and electronics. Each surrogate ships with uncertainty calibration, sensitivity, and unit tests—so engineers know when to trust it (XAI).
Search and optimization with safety constraints. Autonomous Engineering Agents explore design spaces, respect hard limits, and propose explainable trade-offs (XAI).
Mesh/CAD tokenization, shape programs, and topological priors for robust perception of geometry— grounding Industrial Intelligence suggestions in manufacturing reality.
Formalize the physics + data assumptions.
Constrained learning, ablations, and XAI.
Benchmarks, uncertainty calibration, robustness tests.
APIs into the Engineering Platform / Market Intelligence.
Integrating PDE constraints and uncertainty calibration for robust fluid simulations.
Linking micro-structure to system behavior across mechanical and electronic subsystems.
Provenance, approvals, and explainability for agent decisions in regulated industries.
Shared milestones and dedicated IP lanes to accelerate discovery and transfer.
Sovereign Cloud Hosting, secure enclaves, and governance to protect research-grade assets.
Real-world rigs, shared dashboards, and evidence packs for decisions.
Data residency, isolation, and secure deployment options across EU regions.
Lineage, reproducibility, and model cards for every artifact and release.
CFD/FEA pipelines orchestrated with policy agents for scalable physics workloads.
Ontologies linking product, process, and market for end-to-end traceability.
Feature attribution, counterfactuals, and narratives for each decision.
Guardrails for data minimization, PII scanning, and compliance-aware flows.
Signed artifacts, approvals, and immutable logs across the lifecycle.
Production-grade uncertainty bounds for coupled systems.
Linking design intent to outcomes via causal structure.
Learning under label scarcity for 3D meshes and assemblies.
Closed-loop optimization with hard constraints and proofs.