Stochastic Differential Equations
Differential equations that combine deterministic evolution with random forcing to represent systems influenced by continuous uncertainty.
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Digital Twins
Continuously updated computational representations of physical assets or processes used to estimate state, test scenarios and guide decisions.
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Retrieval-Augmented Generation
Systems that retrieve external evidence at query time and supply it to a generative language component before producing an answer.
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Microbial Synthetic Communities
Designed groups of microbial species used to study and engineer ecological interactions with controlled membership and function.
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Space Weather Forecasting
The observation and prediction of solar and geospace disturbances that can affect satellites, navigation, radio and power systems.
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Neuromorphic Computing
Computing architectures that use brain-inspired event-driven neurons, synapses and memory-compute integration.
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Protein Language Models
Machine-learning models that learn statistical representations of amino-acid sequences for prediction and molecular design.
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Topological Data Analysis
Methods that quantify connected components, loops and higher-dimensional voids in data across a range of spatial scales.
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Semiconductor Lithography
The pattern-transfer processes used to define nanoscale electronic structures repeatedly across semiconductor wafers.
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Reinforcement Learning
Machine learning for sequential decisions in which an agent improves behaviour from rewards generated through interaction.
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Optimal Transport
A mathematical framework for transforming one distribution into another while minimising a defined movement cost.
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Chaos Theory
The study of deterministic nonlinear systems whose trajectories can become unpredictable through sensitive dependence on initial conditions.
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Glacier Dynamics
The deformation, sliding and mass exchange that govern how land ice flows from accumulation zones toward its margins.
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Heterogeneous Catalysis
Chemical acceleration at an interface where reactants and catalyst occupy different physical phases.
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Transformer Architectures
Neural-network systems that model relationships among tokens using attention and parallel sequence processing.
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Federated Learning
Distributed machine learning that coordinates model training across data holders without centralising their raw records.
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Causal Inference
Statistical and experimental frameworks for estimating how interventions change outcomes rather than merely describing associations.
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Homomorphic Encryption
Cryptographic methods that permit selected computations on encrypted data without first revealing the plaintext.
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Graph Neural Networks
Machine-learning architectures that learn representations from entities connected by relational structure.
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Bayesian Inference
A framework for updating probability distributions over unknown quantities when new evidence is observed.
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Ocean Circulation
The wind-, buoyancy- and tide-driven movement of seawater that redistributes heat, carbon, nutrients and momentum.
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Artificial Neural Networks
Layered computational models that learn distributed representations by adjusting weighted connections from data.
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