Results 111 to 120 of about 2,297,129 (216)

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

Breakthroughs in the theory of Macdonald polynomials. [PDF]

Proc Natl Acad Sci U S A, 2005
Garsia A, Remmel JB.
europepmc   +1 more source

Gaussian Process Regression–Neural Network Hybrid with Optimized Redundant Coordinates: A New Simple Yet Potent Tool for Scientist's Machine Learning Toolbox

Advanced Intelligent Discovery, EarlyView.
A machine learning method, opt‐GPRNN, is presented that combines the advantages of neural networks and kernel regressions. It is based on additive GPR in optimized redundant coordinates and allows building a representation of the target with a small number of terms while avoiding overfitting when the number of terms is larger than optimal.
Sergei Manzhos, Manabu Ihara
wiley   +1 more source

One Part Leaky Covers. [PDF]

Mathematica (N Y)
Cavalieri R, Markwig H, Schmitt J.
europepmc   +1 more source

A Pure L1-norm Principal Component Analysis. [PDF]

Comput Stat Data Anal, 2013
Brooks J, Dulá J, Boone E.
europepmc   +1 more source

Wall-crossing formulae and strong piecewise polynomiality for mixed Grothendieck dessins d'enfants, monotone and simple double Hurwitz numbers

, 2018
We derive explicit formulae for the generating series of mixed Grothendieck dessins d’enfant/monotone/simple Hurwitz numbers, via the semi-infinite wedge formalism.
M. Anas Hahn, Lewanski D, R. Kramer
core  

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

Hardware acceleration of number theoretic transform for zk‐SNARK

Engineering Reports, EarlyView., 2023
An FPGA‐based hardware accelerator with a multi‐level pipeline is designed to support the large‐bitwidth and large‐scale NTT tasks in zk‐SNARK. It can be flexibly scaled to different scales of FPGAs and has been equipped in the heterogeneous acceleration system with the help of HLS and OpenCL.
Haixu Zhao, Dong Ding, Feng Wang, Pengcheng Hua, Ning Wang, Qin Wu, Zhilei Chai   +6 more
wiley   +1 more source

Learning Phenotypic Associations for Parkinson's Disease with Longitudinal Clinical Records. [PDF]

AMIA Jt Summits Transl Sci Proc
Pan W, Su C, Maasch JRMA, Chen K, Henchcliffe C, Wang F.   +5 more
europepmc   +1 more source