The Machine Learning Platform built for Engineers
Use twinLab to get answers from your simulations and experiments, faster. Augment your existing workflows while still applying your domain knowledge to enable human-in-the-loop decision making backed by uncertainty quantification.
What twinLab can do for engineering workflows
- Predict
Predict unseen scenarios
- Recommend
Optimise your sampling
- Calibrate
Connect models and data
- LLM
Query and understand your data
Make Real-time Predictions with Confidence
- Understand what factors and features drive outcomes
- Even on limited, noisy or inaccurate data
- With your expert opinion integrated
Optimise your Sampling with the Recommend Module
- Reduce cost and the number of samples using Active Learning
- Reduce time spent on slow simulations or running expensive experiments
- Get to the answer with fewer iterations while maintaining confidence
Calibrate Models and Data with ease
- Find best-fit values across all possible inputs and conditions
- Maintain understanding of your system even with partial data
- Determine the critical configuration for recreating any given result
Automate Data Analysis using the twinLab LLM
- Query your data in natural language
- An evidence trail gives explainable results and allows you to make decisions with confidence
- Your in-house expert, trained specifically to answers your questions
twinLab is Enabling the Power of ML across Sectors
- Fusion Energy
Unlocking the Future of Energy
- Nuclear Fission
Supporting Novel Technologies
- Water & Environment
Reducing Both Costs and Pollution
- Renewable Energy
Supporting the Sustainable Transition
- Transport Systems
Increasing Efficiency Across Networks
- Advanced Materials
Enabling Materials to Become Data-Driven
The twinLab ecosystem
Integrate twinLab across your stack using native or API-driven integrations
What makes twinLab so powerful for engineering workflows?
- Deep Gaussian Processes enable highly flexible probabilistic models which naturally quantify uncertainty
- Automated model selection gives sensible defaults, while still allowing you to set expert parameters.
- Our calibration solutions utilise world-recognized research in Multi-level/fidelity Markov Chain Monte Carlo.
- Our approach scales from limited data to highly-dimensional datasets, and even field inputs/outputs
Explore twinLab's documentation
Take a look at our documentation and explore twinLab’s capabilities, interfaces and deployment options.
