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2. Physics-informed neural networks - Wikipedia

   en.wikipedia.org/wiki/Physics-informed_neural...

   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).

3. Machine learning in physics - Wikipedia

   en.wikipedia.org/wiki/Machine_learning_in_physics

   Quantum mechanics. Applying classical methods of machine learning to the study of quantum systems is the focus of an emergent area of physics research. 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 ...

4. Symbolic artificial intelligence - Wikipedia

   en.wikipedia.org/wiki/Symbolic_artificial...

   e. In artificial intelligence, symbolic artificial intelligence (also known as classical artificial intelligence or logic-based artificial intelligence) [1][2] is the term for the collection of all methods in artificial intelligence research that are based on high-level symbolic (human-readable) representations of problems, logic and search. [3]

5. Google researchers claim new breakthrough in getting AI to ...

   www.aol.com/finance/google-researchers-claim...

   The news was an advancement on a system that the AI research lab had unveiled in January, called AlphaGeometry, that could solve geometry problems from the IMO about as well as top high school ...

6. Neural network (machine learning) - Wikipedia

   en.wikipedia.org/wiki/Neural_network_(machine...

   v. t. e. In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a model inspired by the structure and function of biological neural networks in animal brains. [ 1 ][ 2 ] An ANN consists of connected units or nodes called artificial neurons, which loosely model the neurons in the brain.

7. Quantum algorithm - Wikipedia

   en.wikipedia.org/wiki/Quantum_algorithm

   The triangle-finding problem is the problem of determining whether a given graph contains a triangle (a clique of size 3). The best-known lower bound for quantum algorithms is (), but the best algorithm known requires O(N 1.297) queries, [31] an improvement over the previous best O(N 1.3) queries. [21] [32]