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A basic example of this is quantum state tomography, where a quantum state is learned from measurement. [1] Other examples include learning Hamiltonians, [2] [3] learning quantum phase transitions, [4] [5] and automatically generating new quantum experiments.
Physics-informed neural networks for solving Navier–Stokes equations. Physics-informed neural networks (PINNs), [1] also referred to as Theory-Trained Neural Networks (TTNs), [2] are a type of universal function approximators that can embed the knowledge of any physical laws that govern a given data-set in the learning process, and can be described by partial differential equations (PDEs).
For example, there is a prototype, photonic, quantum memristive device for neuromorphic (quantum-)computers (NC)/artificial neural networks and NC-using quantum materials with some variety of potential neuromorphic computing-related applications, [367] [368] and quantum machine learning is a field with some variety of applications under ...
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]
A few examples are energy storage, medical diagnosis, military logistics, applications that predict the result of judicial decisions, foreign policy, or supply chain management. AI applications for evacuation and disaster management are growing. AI has been used to investigate if and how people evacuated in large scale and small scale ...
Simplified example of training a neural network in object detection: The network is trained by multiple images that are known to depict starfish and sea urchins, which are correlated with "nodes" that represent visual features. The starfish match with a ringed texture and a star outline, whereas most sea urchins match with a striped texture and ...
One example is the genetic algorithm for optimizing coefficients of a PID controller [2] or discrete-time optimal control. [ 3 ] Control design as regression problem of the first kind: MLC approximates a general nonlinear mapping from sensor signals to actuation commands, if the sensor signals and the optimal actuation command are known for ...
In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a data set. [1] Choosing informative, discriminating, and independent features is crucial to produce effective algorithms for pattern recognition, classification, and regression tasks.