Engineering & Manufacturing
Automotive & Mobility
Discover governing equations across powertrain, chassis, and battery systems from test and operational data.
One of 34 industries across 8 sectors served by CDE — the Causal Dynamics Engine for Science and Engineering.
The Challenge
Why Automotive & Mobility teams still struggle to explain what is happening
Automotive development produces extensive time-series data across powertrain, chassis, battery, thermal management, and autonomous driving subsystems: dynamometer measurements, track testing telemetry, battery cycling data, sensor fusion logs, and crash test recordings. Interactions between subsystems create compound dynamics that determine vehicle-level performance, efficiency, and safety. Development teams face pressure to reduce validation cycles while expanding the operating envelope of increasingly electrified and automated vehicles, yet extracting governing relationships from test data remains a persistent bottleneck.
Physics-based simulation combined with empirical calibration (engine maps, tire models, battery equivalent circuits) requires extensive tuning per vehicle variant and operating condition. These models encode known physics but cannot reveal unknown dynamics or subsystem interactions. As vehicles incorporate electric powertrains, advanced battery chemistries, and autonomous driving systems, the parameter spaces expand beyond what traditional calibration methods can explore. Manufacturing variability adds further complexity, with poorly characterized relationships between production process variation and field performance.
The CDE Approach
How CDE closes the explanation gap in automotive & mobility
CDE discovers governing equations of automotive system behavior directly from test and operational data. By ingesting dynamometer measurements, battery cycling profiles, chassis dynamics recordings, and sensor data, it identifies mathematical relationships describing how vehicles actually perform, including interaction effects between subsystems that compartmentalized analysis misses. Every discovered equation is a typed scientific claim with confidence bounds and provenance, providing traceable, reproducible dynamics models grounded in measured behavior.
Causal mode separates manufacturing variability from design effects, enabling root-cause analysis for quality issues and warranty claims. Regime classification detects operating-mode transitions (battery thermal runaway precursors, tire grip limit transitions, powertrain efficiency regime changes) and characterizes governing dynamics within each regime. Symbolic mode extracts closed-form equations for combustion dynamics, battery degradation, and vehicle dynamics. Deterministic replay meets the documentation and traceability standards of automotive development and homologation.
Discovery Engine
How CDE applies here
Neuro-Symbolic discovery handles complex multi-domain interactions through the engine's internal methods while producing interpretable models for engineering review and calibration tools. Symbolic mode extracts closed-form equations for control system design and simulation calibration. Causal mode is particularly valuable for quality and reliability investigations, distinguishing whether field failures stem from design factors, manufacturing variation, or usage patterns. Conservation law detection validates energy balance across powertrain subsystems, identifying measurement errors and unmodeled losses.
Causal Graphs
Discovers directed causal structure from observational data — identifiable causal graphs, regime classifications, and intervention predictions.
Governing Equations
Extracts compact governing laws grounded in the causal structure — interpretable equations your team can read, verify, and compare against known theory.
Intervention Design
Proposes targeted experiments to resolve ambiguous causal edges — maximizing information gain where the causal structure is still uncertain.
Causal Validation
Negative controls, falsification tests, and identifiability analysis applied to every causal claim before promotion to the evidence ledger.
Typed Scientific Claims
What CDE discovers
Every discovery CDE produces is a typed scientific claim — not a black-box prediction, but a governed, reproducible, auditable piece of scientific knowledge with full provenance.
- Combustion dynamics equations
- Battery degradation models
- Vehicle dynamics governing laws
- Tire-road interaction models
- Autonomous driving perception laws
Governed Discovery
Make the finding reviewable
Every discovery CDE produces carries the review context around it: a Truth Dial setting, an evidence entry with replay context, and control results including bootstrap stability, out-of-distribution testing, and feature-shuffle validation.
For automotive & mobility, that means teams can compare runs, justify decisions, and decide whether a finding is ready for internal use, external review, or regulated submission.
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Put CDE on a real automotive & mobility problem
Whether you are exploring automotive & mobility data for the first time or scaling an existing research programme, CDE adapts to your workflow. Bring the dataset, the decision pressure, and the constraints. We will map the right discovery path.