Results 61 to 70 of about 8,190 (293)

Composite optimization with coupling constraints via penalized proximal gradient method in asynchronous networks [PDF]

, 2023
In this paper, we consider a composite optimization problem with linear coupling constraints in a multi-agent network. In this problem, the agents cooperatively optimize a strongly convex cost function which is the linear sum of individual cost functions
Wang, Jianzheng, Hu, Guoqiang
core   +1 more source

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

Optimization of Collaborative Vessel Scheduling for Offshore Wind Farm Installation Under Weather Uncertainty

Journal of Marine Science and Engineering
The construction cost of offshore wind farms (OWFs) is heavily influenced by vessel scheduling and meteorological uncertainties. To address these challenges, this paper proposes a constraint-driven hierarchical optimization framework for the coordinated ...
Shengguan Qu, Changmao Yu, Yang Zhou, Yi Hou, Jianhua Wang, Fenglei Li   +5 more
doaj   +1 more source

On variational solutions for whole brain serial-section histology using a Sobolev prior in the computational anatomy random orbit model.

PLoS Computational Biology, 2018
This paper presents a variational framework for dense diffeomorphic atlas-mapping onto high-throughput histology stacks at the 20 μm meso-scale. The observed sections are modelled as Gaussian random fields conditioned on a sequence of unknown section by ...
Brian C Lee, Daniel J Tward, Partha P Mitra, Michael I Miller   +3 more
doaj   +1 more source

On the Calculation of the Brinkman Penalization Term in Density-Based Topology Optimization of Fluid-Dependent Problems

CoRR, 2023
In topology optimization of fluid-dependent problems, there is a need to interpolate within the design domain between fluid and solid in a continuous fashion. In density-based methods, the concept of inverse permeability in the form of a volumetric force is utilized to enforce zero fluid velocity in non-fluid regions.
Mohamed Abdelhamid 0003, Aleksander Czekanski   +1 more
openaire   +2 more sources

Topology and Material Optimization in Ultra‐Soft Magneto‐Active Structures: Making Advantage of Residual Anisotropies

Advanced Materials, EarlyView.
Residual magnetization induces pronounced mechanical anisotropy in ultra‐soft magnetorheological elastomers, shaping deformation and actuation even without external magnetic fields. This study introduces a computational‐experimental framework integrating magneto‐mechanical coupling into topology optimization for designing soft magnetic actuators with ...
Carlos Perez‐Garcia, Rogelio Ortigosa, Jesús Martínez‐Frutos, Daniel Garcia‐Gonzalez   +3 more
wiley   +1 more source

Simulated Data for Linear Regression with Structured and Sparse Penalties: Introducing pylearn-simulate

Journal of Statistical Software, 2018
A currently very active field of research is how to incorporate structure and prior knowledge in machine learning methods. It has lead to numerous developments in the field of non-smooth convex minimization. With recently developed methods it is possible
Tommy Löfstedt, Vincent Guillemot, Vincent Frouin, Edouard Duchesnay, Fouad Hadj-Selem   +4 more
doaj   +1 more source

An entropy penalized approach for stochastic control problems. Complete version [PDF]

International audienceIn this paper, we propose an original approach to stochastic control problems. We consider a weak formulation that is written as an optimization (minimization) problem on the space of probability measures.
Russo, Francesco, Oudjane, Nadia, Bourdais, Thibaut   +2 more
core   +1 more source

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +1 more source

Advancing Lithium–Oxygen Batteries: Pioneering Cathode Catalyst Innovation and Artificial Intelligence‐Driven Design Paradigms

Advanced Materials, EarlyView.
This review summarizes the principles and challenges of nonaqueous lithium‐oxygen batteries and recent advances in cathode catalysts, including carbon‐based materials, metals, oxides, sulfides, nitrides, carbides, and redox mediators. It highlights emerging design strategies and artificial intelligence‐driven approaches, emphasizing data‐assisted ...
Yuqing Yao, Chongyang Hao, Karol Viviana Mejia‐Centeno, Malik Dilshad Khan, Shang Wang, Longqiu Li, Yanhong Tian, Andreu Cabot, Qing Sun   +8 more
wiley   +1 more source