Results 151 to 160 of about 144,645 (228)

Hierarchical bayesian fusion of inspection and monitoring data for probabilistic bridge deterioration assessment. [PDF]

Sci Rep
Wang B, Chen K, Wang B.
europepmc   +1 more source

Artificial Intelligence for Multiscale Modeling in Solid‐State Physics and Chemistry: A Comprehensive Review

Advanced Intelligent Systems, EarlyView.
This review explores the transformative impact of artificial intelligence on multiscale modeling in materials research. It highlights advancements such as machine learning force fields and graph neural networks, which enhance predictive capabilities while reducing computational costs in various applications.
Artem Maevskiy, Vitalii Kapitan, Andrey Ustyuzhanin   +2 more
wiley   +1 more source

Integrating Large Language Models into Traffic Systems: Integration Levels, Capability Boundaries, and an Information-Theoretic Perspective. [PDF]

Entropy (Basel)
Tu W, Li J, Xiao F, Wang X, Lu Y.
europepmc   +1 more source

A Flexible and Energy‐Efficient Compute‐in‐Memory Accelerator for Kolmogorov–Arnold Networks

Advanced Intelligent Systems, EarlyView.
This article presents KA‐CIM, a compute‐in‐memory accelerator for Kolmogorov–Arnold Networks (KANs). It enables flexible and efficient computation of arbitrary nonlinear functions through cross‐layer co‐optimization from algorithm to device. KA‐CIM surpasses CPU, ASIC, VMM‐CIM, and prior KAN accelerators by 1–3 orders of magnitude in energy‐delay ...
Chirag Sudarshan, Oliver Artner, Paul‐Philipp Manea, Sebastian Siegel, Susanne Hoffmann‐Eifert, Regina Dittmann, John Paul Strachan   +6 more
wiley   +1 more source

Integrating biological and machine learning models for rainbow trout growth: Balancing accuracy and interpretability. [PDF]

PLoS One
Fulton L, Lyu P.
europepmc   +1 more source

Deep Learning Methods for Assessing Time‐Variant Nonlinear Signatures in Clutter Echoes

Advanced Intelligent Systems, EarlyView.
Motion classification from biosonar echoes in clutter presents a fundamental challenge: extracting structured information from stochastic interference. Deep learning successfully discriminates object speed and direction from bat‐inspired signals, achieving 97% accuracy with frequency‐modulated calls but only 48% with constant‐frequency tones. This work
Ibrahim Eshera, Gaurav Duggal, Rolf Müller   +2 more
wiley   +1 more source

The Role of Chemistry Across Disciplines From Humanities to Life Sciences in Understanding Complexity and Emergence

Angewandte Chemie, EarlyView.
This study explores the origins of life by linking prebiotic chemistry, the emergence of information‐carrying molecules such as RNA and proteins, and philosophical questions about consciousness. The study emphasizes the role of molecular evolution in the Central Dogma and provides insights into the chemical origins of biology and the basis of life's ...
Harald Schwalbe, Josef Wachtveitl, Alexander Heckel, Florian Buhr, Sabrina Toews, Thomas M. Schimmer   +5 more
wiley   +2 more sources

Bayesian deep learning for probabilistic aquifer vulnerability and uncertainty prediction. [PDF]

Sci Rep
Mengistu TD, Kim MG, Chung IM, Chang SW.
europepmc   +1 more source

Methods for Setting Device Specifications for Analog In‐Memory Computing Inference

Advanced Intelligent Systems, EarlyView.
Non‐volatile memories (NVMs) are being developed for analog in‐memory computing for energy‐efficient, high‐speed deep learning inference. As technology is moving to industry adoption, a method to define required NVM specifications is critical for improving performance and reducing manufacturing cost.
Zhenyu Wu, Xin Su, Malte J. Rasch, Ning Li   +3 more
wiley   +1 more source

Residual bayesian attention networks for uncertainty quantification in regression tasks. [PDF]

Sci Rep
Chen Y, Guan W, Azzam R.
europepmc   +1 more source