AIMC Topic: Physics

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Deep learning-accelerated computational framework based on Physics Informed Neural Network for the solution of linear elasticity.

Neural networks : the official journal of the International Neural Network Society Mar 13, 2023
The paper presents an efficient and robust data-driven deep learning (DL) computational framework developed for linear continuum elasticity problems. The methodology is based on the fundamentals of the Physics Informed Neural Networks (PINNs). For an...
Neural Networks, Computer Deep Learning Elasticity Physics
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Protein Design Using Physics Informed Neural Networks.

Biomolecules Mar 1, 2023
The inverse protein folding problem, also known as protein sequence design, seeks to predict an amino acid sequence that folds into a specific structure and performs a specific function. Recent advancements in machine learning techniques have been su...
Neural Networks, Computer Proteins Molecular Dynamics Simulation Amino Acid Sequence Physics
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Deep learning based unpaired image-to-image translation applications for medical physics: a systematic review.

Physics in medicine and biology Feb 23, 2023
. There is a growing number of publications on the application of unpaired image-to-image (I2I) translation in medical imaging. However, a systematic review covering the current state of this topic for medical physicists is lacking. The aim of this a...
Algorithms Deep Learning Ultrasonography Magnetic Resonance Imaging Physics
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Combining multi-objective genetic algorithm and neural network dynamically for the complex optimization problems in physics.

Scientific reports Jan 17, 2023
Neural network (NN) has been tentatively combined into multi-objective genetic algorithms (MOGAs) to solve the optimization problems in physics. However, the computationally complex physical evaluations and limited computing resources always cause th...
Humans Neural Networks, Computer Algorithms Physics
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Constructing Physics-Informed Neural Networks with Architecture Based on Analytical Modification of Numerical Methods by Solving the Problem of Modelling Processes in a Chemical Reactor.

Sensors (Basel, Switzerland) Jan 6, 2023
A novel type of neural network with an architecture based on physics is proposed. The network structure builds on a body of analytical modifications of classical numerical methods. A feature of the constructed neural networks is defining parameters o...
Neural Networks, Computer Physics
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Predicting stress, strain and deformation fields in materials and structures with graph neural networks.

Scientific reports Dec 17, 2022
Developing accurate yet fast computational tools to simulate complex physical phenomena is a long-standing problem. Recent advances in machine learning have revolutionized the way simulations are approached, shifting from a purely physics- to AI-base...
Neural Networks, Computer Machine Learning Physical Examination Physics
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Physics-informed machine learning methods for biomass gasification modeling by considering monotonic relationships.

Bioresource technology Dec 9, 2022
Machine learning methods have recently shown a broad application prospect in biomass gasification modeling. However, a significant drawback of the machine learning approaches is their poor physical interpretability when relying on limited experimenta...
Neural Networks, Computer Machine Learning Biomass Models, Theoretical Gases Physics
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Path sampling of recurrent neural networks by incorporating known physics.

Nature communications Nov 24, 2022
Recurrent neural networks have seen widespread use in modeling dynamical systems in varied domains such as weather prediction, text prediction and several others. Often one wishes to supplement the experimentally observed dynamics with prior knowledg...
Neural Networks, Computer Artificial Intelligence Algorithms Monte Carlo Method Physics
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Elasticity imaging using physics-informed neural networks: Spatial discovery of elastic modulus and Poisson's ratio.

Acta biomaterialia Nov 17, 2022
Elasticity imaging is a technique that discovers the spatial distribution of mechanical properties of tissue using deformation and force measurements under various loading conditions. Given the complexity of this discovery, most existing methods appr...
Humans Neural Networks, Computer Diagnostic Imaging Stress, Mechanical Elasticity Finite Element Analysis Physics Elastic Modulus
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Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management.

Scientific reports Nov 4, 2022
Avoiding over-pressurization in subsurface reservoirs is critical for applications like CO[Formula: see text] sequestration and wastewater injection. Managing the pressures by controlling injection/extraction are challenging because of complex hetero...
Neural Networks, Computer Machine Learning Uncertainty Physics
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