Results 161 to 170 of about 336,714 (315)

Anomaly-Detection-Driven Screening of Thermodynamic Stability from Composition Descriptors Alone. [PDF]

J Phys Chem Lett
Makino K, Yamaguchi Y, Tanibata N, Takeda H, Kobayashi R, Karasuyama M, Nakayama M.   +6 more
europepmc   +1 more source

Evaluation results of translation ATE RMSE (unit: m) on long corridor scene and the underground garage scene. Bold numbers represent the best performances.

Gaochao Yang (18458505), Pengfei Liu (136115), Weifeng Ma (3995396)   +2 more
openalex   +1 more source

Universally Accurate or Specifically Inadequate? Stress‐Testing General Purpose Machine Learning Interatomic Potentials

Advanced Intelligent Discovery, EarlyView.
We investigate MACE‐MP‐0 and M3GNet, two general‐purpose machine learning potentials, in materials discovery and find that both generally yield reliable predictions. At the same time, both potentials show a bias towards overstabilizing high energy metastable states. We deduce a metric to quantify when these potentials are safe to use.
Konstantin S. Jakob, Karsten Reuter, Johannes T. Margraf   +2 more
wiley   +1 more source

Advancing operational global aerosol forecasting with machine learning. [PDF]

Nature
Gui K, Zhang X, Che H, Che H, Li L, Zheng Y, An L, Miao Y, Zhao H, Dubovik O, Holben B, Wang J, Gupta P, Lind ES, Toledano C, Wang H, Wang Z, Wang Y, Huang X, Dai K, Xia X, Xu X, Zhang X.   +22 more
europepmc   +1 more source

Relative absolute bias and RMSE of estimated shape parameter for q = 0.5.

Laxmi Prasad Sapkota (21519298), Nirajan Bam (21519301), Vijay Kumar (377076)   +2 more
openalex   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

Estimating Trends With Differential Item Functioning: A Comparison of Five IRT-Based Approaches. [PDF]

Educ Psychol Meas
Engels O, Lüdtke O, Robitzsch A.
europepmc   +1 more source

Trade-Space Exploration Comparison of Parametric Cost Models for Satellite Anomalies with RMSE and RRMSE

Muhammad Aryss Zafran Shahrul Ariffin, Nadirah Abdul Rahim, Abdul Mutholib   +2 more
openalex   +2 more sources

Deep Learning Prediction of Surface Roughness in Multi‐Stage Microneedle Fabrication: A Long Short‐Term Memory‐Recurrent Neural Network Approach

Advanced Intelligent Discovery, EarlyView.
A sequential deep learning framework is developed to model surface roughness progression in multi‐stage microneedle fabrication. Using real‐world experimental data from 3D printing, molding, and casting stages, an long short‐term memory‐based recurrent neural network captures the cumulative influence of geometric parameters and intermediate outputs ...
Abdollah Ahmadpour, Sara Soltanabadi Farshi, Tuanna Ozcan, Amir Mohammad Ketabchi, Alper Kiraz, Savas Tasoglu   +5 more
wiley   +1 more source

Downscaling the spatial resolution of satellite imagery based on morphometric parameters to estimate the Topographic Wetness Index using GIS tools. [PDF]

Sci Rep
Shabbir H, Ehsan M, Raza D, Sohail MT, Alqahtani H, Almutairi R, Almutairi Y.   +6 more
europepmc   +1 more source