Antimatters Labs
Antimatters Labs
Physics × AI, built for real systems.
Each major leap in engineering has come from compressing the loop between an idea and a verified result.
First principles gave us structure.
Simulation gave us fidelity.
Sensors gave us data.
Today's workflows weren't built for learning.
AI is advancing fast, but the modeling loop hasn't kept up.
High-fidelity simulation is still too slow for iteration.
Pure black-box models still break when regimes shift.
In practice, the hard part isn't getting a model to fit.
It's getting a model you can trust.
Physics-informed machine learning made the promise explicit: fuse data with structure.
It also made the bottlenecks explicit: training can be pathological, and "good test error" is not the same as deployment-grade reliability.
We think that's backwards.
Antimatters builds hybrid, trustworthy models of dynamics.
We're an applied research and product lab building dynamical digital twins—models that learn from operational data while preserving the parts of physics you already know.
Our starting point is the hybrid SciML view: learn what's missing, keep what's true.
Behind the scenes, we're solving these core problems:
- Hybrid differential-equation learning— keep a mechanistic core; learn residual structure without losing identifiability.
- Training stability— understand and fix failure-to-train dynamics in constraint-driven objectives.
- Stiff and multiscale regimes— make learning work when numerical realities dominate the problem.
- Fast surrogates for parametric PDE families— operator learning for rapid inference when simulation is the bottleneck.
- Interpretability when it changes decisions— extract compact symbolic structure from learned residuals, and evaluate it against modern SR benchmarks.
Antimatters Labs is where research meets deployment.
A new category is forming around AI-native engineering: teams are rebuilding the engineering stack because the economics are clear—faster iteration, fewer redesign loops, better decisions.
Meanwhile, the tooling is maturing toward scalable Physics-AI training and inference.
We focus on the missing middle: turning SciML into models and workflows that survive reality—noise, drift, regime shifts, and operational constraints.
If you're exploring adjacent questions in SciML, dynamical systems, operator learning, symbolic regression, uncertainty, or deployment, we'd like to connect.
Contact: samarth@antimatterslabs.com