Models for physical systems
General models that learn from observations, simulations, and physical constraints across scientific domains.
Physics-first AI
Machine learning for physical systems.
Summer Intelligence builds models for understanding, simulating, and designing physical systems — with reliability evaluated from the start.
Research
From physical structure to reliable use.
We combine data with physical structure to build more useful models for science and engineering.
Learned simulation
Models for prediction, forecasting, and design across climate, energy, materials, and engineering.
Evaluation & interpretability
Methods for measuring reliability, uncertainty, and failure modes before models are used in higher-impact settings.
Open questions
Questions guiding our work.
These questions connect model design, generalization, interpretability, and evaluation.
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Which forms of knowledge transfer across physical systems?
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When do physical constraints improve generalization and reliability?
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How can we distinguish physical structure from dataset-specific correlations?
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What evidence is required before real-world use?
Safety
Evidence should match the risk.
Models used in science and engineering need context-specific evaluation. We build safety checks into development and deployment.
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S·01
Evaluate before deployment
Define failure modes, stress tests, and stopping criteria before deployment.
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S·02
Interpret alongside evaluation
Use behavioral tests and interpretability together to understand limits.
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S·03
Stage access with risk
Begin in constrained settings and expand access only after review.
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S·04
Report material limitations
Report material limitations and negative results alongside positive findings.
About
Focused on physical systems.
Summer Intelligence develops models for physical systems. We combine physics and machine learning to understand, simulate, and design them.
Our approach uses physical structure — including symmetry, locality, and conservation — where it improves data efficiency, generalization, or interpretability.
Our work focuses on useful models, careful evaluation, and clear reporting.
Contact us
Start a conversation.
For research, collaboration, or general inquiries, send us a note.