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.

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What twinLab can do for engineering workflows

Predict

Predictions 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
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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
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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
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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
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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

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The twinLab ecosystem

Integrate twinLab across your stack using native or API-driven integrations

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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
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