Results 241 to 250 of about 109,088 (302)

Research on a Phase-Shift-Based Discontinuous PWM Method for 24V Onboard Thermally Limited Micro Voltage Source Inverters. [PDF]

Micromachines (Basel)
Wang S, Xia C.
europepmc   +1 more source

Comparison of DeePMD, MTP, GAP, ACE and MACE Machine‐Learned Potentials for Radiation‐Damage Simulations: A User Perspective

Advanced Intelligent Discovery, EarlyView.
The authors evaluated six machine‐learned interatomic potentials for simulating threshold displacement energies and tritium diffusion in LiAlO2 essential for tritium production. Trained on the same density functional theory data and benchmarked against traditional models for accuracy, stability, displacement energies, and cost, Moment Tensor Potential ...
Ankit Roy, Ram Devanathan, Sarah Allec, Giridhar Nandipati, Andrew M. Casella, David J. Senor, Duane D. Johnson, Ganesh Balasubramanian, Ayoub Soulami   +8 more
wiley   +1 more source

Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning

Advanced Intelligent Discovery, EarlyView.
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley   +1 more source

Temperature Evolution of Crystal Structure, Ferroelectricity and Ionic Conductivity of Ca₉La(VO₄)₇. [PDF]

Molecules
Baryshnikova OV, Lazoryak BI, Morozov VA, Stefanovich SY, Mosunov AV, Gallyamov EM, Aksenov SM, Deyneko DV.   +7 more
europepmc   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

Motion Artifacts Removal from Measured Arterial Pulse Signals at Rest: A Generalized SDOF-Model-Based Time-Frequency Method. [PDF]

Sensors (Basel)
Hao Z.
europepmc   +1 more source

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties With Phonon‐Informed Datasets

Advanced Intelligent Discovery, EarlyView.
Phonons‐informed machine‐learning predictive models are propitious for reproducing thermal effects in computational materials science studies. Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost.
Pol Benítez, Cibrán López, Edgardo Saucedo, Teruyasu Mizoguchi, Claudio Cazorla   +4 more
wiley   +1 more source

A Residual Amplitude Modulation Noise Suppression Method Based on Multi-Harmonic Component Decoupling. [PDF]

Sensors (Basel)
Luo Q, Su H, Wang Y, Yang C.
europepmc   +1 more source

Reinforcement learning-driven model predictive control for optimizing counter-rotating permanent magnet synchronous motor in submarine propulsion system. [PDF]

Sci Rep
Delelew EY, Dulecha KA, Ararso ZT, Abdissa CM.   +3 more
europepmc   +1 more source

Experimental Insights into Islanding Detection in PV Inverters: Foundations for a Parallel-Operation Test Standard. [PDF]

Sensors (Basel)
Chmielowiec K, Piszczek A, Topolski Ł.
europepmc   +1 more source