Results 131 to 140 of about 1,052,376 (334)

Machine Learning‐Assisted Inverse Design of Soft and Multifunctional Hybrid Liquid Metal Composites

Advanced Functional Materials, EarlyView.
A machine learning framework is presented for inverse design of synthesizable multifunctional composites containing both liquid metal and solid inclusions. By integrating physics‐based modeling, data‐driven prediction, and Bayesian optimization, the approach enables intelligent design of experiments to identify optimal compositions and realize these ...
Lijun Zhou, Yunsik Ohm, Ren‐Mian Chin, Olivia Kerr, Krithika Manohar, Mohammad H. Malakooti   +5 more
wiley   +1 more source

Z-Score Experience Replay in Off-Policy Deep Reinforcement Learning

Sensors
Reinforcement learning, as a machine learning method that does not require pre-training data, seeks the optimal policy through the continuous interaction between an agent and its environment.
Yana Yang, Meng Xi, Huiao Dai, Jiabao Wen, Jiachen Yang   +4 more
doaj   +1 more source

Adaptive fault diagnosis and resolution system for enterprise data replication system using deep reinforcement learning

, 2022
Modern business IT systems in large organisations have high levels of collaboration and interoperability to support various business functions. In heterogeneous IT systems, data is one of the most important entities that are constantly exchanged.
Wee, Chee Keong
core   +1 more source

3D Printing Innovations in Polymeric Porous and Patterned Architecture

Advanced Functional Materials, EarlyView.
Polymeric foams occupy a unique structural space between dense solids and open networks, where engineered void fraction governs mechanical compliance, thermal resistance, and mass transport. Additive manufacturing now enables precise spatial control over cellular architecture, unlocking designer foam structures across applications spanning crash ...
Dhanush Patil, Sri Vaishnavi Thummalapalli, Clarissa Westover, Arunachalam Ramanathan, M. Taylor Sobzcak, Roumika Patil, Qixuan Xiang, Tiantian Li, Sui Yang, Timothy E. Long, Hongyue Sun, Xianqiao Wang, Yang Liu, Kenan Song   +13 more
wiley   +1 more source

Designed Lewis Acid–Base Passivation for High Performance Perovskite Solar Cells

Advanced Functional Materials, EarlyView.
ABSTRACT Silicon's high cost and long energy payback time remain major barriers to the global expansion of solar power. In contrast, metal–halide perovskites offer abundant, solution‐processable absorbers, and have achieved efficiencies of 25%–30%, positioning them as strong competitors to silicon.
Afna Manaf, Hiba Shahulhameed, Bhabani Sankar Swain, Shengzhong (Frank) Liu, Adel Najar   +4 more
wiley   +1 more source

Electro‐Steric Ion Confinement in Polyelectrolyte Networks for Robust Nonvolatile Artificial Synapse

Advanced Functional Materials, EarlyView.
Polyelectrolyte stoichiometry governs ion transport and retention in electrolyte‐gated synaptic transistors. A PSS‐rich network creates electro‐steric ion confinement that suppresses ion back‐diffusion and stabilizes channel doping, enabling robust nonvolatile synaptic memory, linear weight updates, and low‐energy operation.
Donghwa Lee, Jinbo Kim, Myeongjin An, Jisoo Park, Junho Sung, Eunsung Hwang, Suhwan Seok, Seong‐Min Bak, Jeonghun Kim, Eunho Lee   +9 more
wiley   +1 more source

A Comprehensive Study on Reinforcement Learning and Deep Reinforcement Learning Schemes

Sir Syed University Research Journal of Engineering and Technology
Reinforcement learning (RL) has emerged as a powerful tool for creating artificial intelligence systems (AIS) and solving problems which require sequential decision-making. Reinforcement learning has achieved some impressive achievements in recent years,
Muhammad Azhar, Mansoor Ahmed Khuhro, Muhammad Waqas, Umair Saeed, Mehar Khan Niazi   +4 more
doaj   +1 more source

The advancements and applications of deep reinforcement learning in Go [PDF]

ITM Web of Conferences
Combining Deep Learning's perceptual skills with Reinforcement Learning's decision-making abilities, Deep Reinforcement Learning (DRL) represents a significant breakthrough in Artificial Intelligence (AI).
Zheng Xutao
doaj   +1 more source

A Deep Reinforcement Learning based Homeostatic System for Unmanned Position Control [PDF]

, 2019
Deep Reinforcement Learning (DRL) has been proven to be capable of designing an optimal control theory by minimising the error in dynamic systems. However, in many of the real-world operations, the exact behaviour of the environment is unknown.
Anjum, Ashiq, Warren Manning, Bower, Craig, Priyanthi M. Dassanayake, Ashiq Anjum, Dassanayake, Priyanthi, Craig Bower, Manning, Warren   +7 more
core   +1 more source

Genipin‐Crosslinked, Silane‐Anchored 3D Tumor–Stroma Microtissues for High‐Content On‐Chip Drug Testing

Advanced Healthcare Materials, EarlyView.
We describe a microfluidic tumor‐stroma co‐culture model, engineered to resist collagen‐hydrogel contraction driven by fibroblast activity. Surface silanization with APTES covalently anchors the matrix to the chip, while Genipin crosslinking progressively increases stiffness and elasticity without harming cells. This supports >10 days of co‐culture and
Doriane Le Manach, Reza Kowsari‐Esfahan, Emilia Reszczyńska, Philippe Nghe, Matthias Nees   +4 more
wiley   +1 more source