Results 71 to 80 of about 946,507 (289)

Learning Transformed Dynamics for Efficient Control Purposes

Mathematics
Learning linear and nonlinear dynamical systems from available data is a timely topic in scientific machine learning. Learning must be performed while enforcing the numerical stability of the learned model, the existing knowledge within an informed or ...
Chady Ghnatios, Joel Mouterde, Jerome Tomezyk, Joaquim Da Silva, Francisco Chinesta   +4 more
doaj   +1 more source

Learning a DFT-based sequence with reinforcement learning: a NAO implementation

Paladyn, 2012
The implementation of sequence learning in robotic platforms offers several challenges. Deciding when to stop one action and continue to the next requires a balance between stability of sensory information and, of course, the knowledge about what action ...
Durán Boris, Lee Gauss, Lowe Robert
doaj   +1 more source

A combined machine learning and density functional theory study of binary Ti-Nb and Ti-Zr alloys: Stability and Young’s modulus

Computational Materials Science, 2020
Abstract The multicomponent Ti alloys, specifically the β -phase, have experienced a strong growth over the last decades, due to their outstanding properties of ultra-high strength and low Young’s modulus. These properties play a significant role in many aerospace and biomedical applications.
Sangqi Xiong, Xin Li, Xiangwei Wu, Jin Yu, Oleg I. Gorbatov, Igor Di Marco, Paul R.C. Kent, Weiwei Sun   +7 more
openaire   +2 more sources

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

Advanced Engineering Materials, EarlyView.
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt, Varma Kvvssn, Hossein Laieghi, Zia Uddin, Peyman Ansari, E. Kıvanç Sadak, Can Barış Toprak, Hüseyin Kızıl, Enrico Salvati   +8 more
wiley   +1 more source

The Cavity Approach to Noisy Learning in Nonlinear Perceptrons

, 2001
We analyze the learning of noisy teacher-generated examples by nonlinear and differentiable student perceptrons using the cavity method. The generic activation of an example is a function of the cavity activation of the example, which is its activation ...
Luo, Peixun, Wong, K. Y. Michael
core   +2 more sources

Stability and NMR Chemical Shift of Amorphous Precursors of Methane Hydrate: Insights from Dispersion-Corrected Density Functional Theory Calculations Combined with Machine Learning

The Journal of Physical Chemistry B, 2020
Clathrate hydrates of natural gases are important backup energy sources. It is thus of great significance to explore the nucleation process of hydrates. Hydrate clusters are building blocks of crystalline hydrates and represent the initial stage of hydrate nucleation.
Keyao Li, Pengju Wang, Lingli Tang, Ruili Shi, Yan Su, Jijun Zhao   +5 more
openaire   +3 more sources

A Topology Optimization Framework for the Inverse Design of Nonlinear Mechanical Metamaterials

Advanced Engineering Materials, EarlyView.
This work uses topology optimization to design unit cells for mechanical metamaterials with a prescribed nonlinear stress–strain response. The framework adds contact and postbuckling modeling to synthesize microstructures for three highly nonlinear responses, including pseudoductile behavior, monostable with snap‐through buckling, and bistable ...
Charlie Aveline, Matthew Santer, Robert Hewson   +2 more
wiley   +1 more source

SMC-Based, Stability-Guided Deep Reinforcement Learning for Control of Uncertain Nonlinear Dynamic Systems

IEEE Access
This paper presents a novel control framework that integrates the stability insights of traditional control with reinforcement learning (RL) to tackle uncertain nonlinear dynamic systems.
Mahya Ramezani, M. Amin Alandihallaj, Andreas M. Hein, Holger Voos   +3 more
doaj   +1 more source

Compositional descriptor-based recommender system accelerating the materials discovery

, 2017
Structures and properties of many inorganic compounds have been collected historically. However, it only covers a very small portion of possible inorganic crystals, which implies the presence of numerous currently unknown compounds.
Hayashi, Hiroyuki, Seko, Atsuto, Tanaka, Isao   +2 more
core   +1 more source

Li$_x$CoO$_2$ phase stability studied by machine learning-enabled scale bridging between electronic structure, statistical mechanics and phase field theories

, 2021
Li$_xTM$O$_2$ (TM={Ni, Co, Mn}) are promising cathodes for Li-ion batteries, whose electrochemical cycling performance is strongly governed by crystal structure and phase stability as a function of Li content at the atomistic scale. Here, we use Li$_x$CoO$_2$ (LCO) as a model system to benchmark a scale-bridging framework that combines density ...
Teichert, Gregory H., Das, Sambit, Aykol, Muratahan, Gopal, Chirranjeevi, Gavini, Vikram, Garikipati, Krishna   +5 more
openaire   +2 more sources