PYTHIA
Scientific Machine Learning for Interpretable Surrogate Intelligence
From scientific question to interpretable decision — with evidence and uncertainty visible at every step.
Enter processQuestion
Define the phenomenon, decision context, constraints, and measurable target before selecting a model.
- Scientific objective and hypothesis
- Decision boundary and constraints
- Observable quantities and success criteria
Knowledge
Assemble domain structure, prior results, provenance, and physical constraints into a queryable context.
- Literature and experimental context
- Knowledge graphs and semantic relations
- Assumptions priors and governing physics
Methods
Select an interpretable modeling strategy that matches the question, available evidence, and required fidelity.
- Surrogate and reduced-order models
- Physics-informed machine learning
- Calibration and uncertainty quantification
Evidence
Test the model against observations, simulations, and validation contracts while preserving provenance.
- Training and validation evidence
- Residuals sensitivity and failure modes
- Traceable model and data lineage
Prediction
Produce fast, uncertainty-aware estimates across the relevant parameter and design space.
- Interpretable response surfaces
- Confidence and extrapolation warnings
- Interactive scenario exploration
Decision
Translate predictions into transparent options, trade-offs, and the next informative action.
- Ranked options and consequences
- Evidence-linked recommendations
- Feedback into the next question
Continuous learning
Every decision returns new evidence to the system and begins a better question.
Explore the method system →