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Publication search results
found 88 matches
- 2024
- Xiaoliang Pan, Ryan Snyder, Jia-Ning Wang, Chance Lander, Carly Wickizer, Richard Van, Andrew Chesney, Yuanfei Xue, Yuezhi Mao, Ye Mei, Jingzhi Pu, Yihan Shao:
Training machine learning potentials for reactive systems: A Colab tutorial on basic models. J. Comput. Chem. 45(10): 638-647 (2024) - Luis Itza Vazquez-Salazar, Silvan Käser, Markus Meuwly:
Outlier-Detection for Reactive Machine Learned Potential Energy Surfaces. CoRR abs/2402.17686 (2024) - 2023
- Minh Thanh Chung, Josef Weidendorfer, Karl Fürlinger, Dieter Kranzlmüller:
From reactive to proactive load balancing for task-based parallel applications in distributed memory machines. Concurr. Comput. Pract. Exp. 35(24) (2023) - Izaz Ullah Khan, Jehanzeb Ali Shah, Muhammad Bilal, Faiza, Muhammad Saqib Khan, Sajid Shah, Ali Akgül:
Machine learning modelling of removal of reactive orange RO16 by chemical activated carbon in textile wastewater. J. Intell. Fuzzy Syst. 44(5): 7977-7993 (2023) - Viktor Pristas, L'ubomír Antoni, Gabriel Semanisin:
Automatic Reactive Power Classification Based on Selected Machine Learning Methods. ICAART (3) 2023: 100-107 - Njegos Jankovic, Javier Roldán-Pérez, Milan Prodanovic, Salvatore D'Arco, Jon Are Suul, Luis Rouco:
Decoupled Active and Reactive Power Controllers for Damping Low-Frequency Oscillations using Virtual Synchronous Machines. ISGT EUROPE 2023: 1-5 - Andrew Adamatzky, Anna Nikolaidou, Antoni Gandia, Alessandro Chiolerio, Mohammad Mahdi Dehshibi:
Reactive Fungal Wearable. Fungal Machines 2023: 93-104 - Anna Nikolaidou, Neil Phillips, Michail-Antisthenis I. Tsompanas, Andrew Adamatzky:
Reactive Fungal Insoles. Fungal Machines 2023: 131-147 - Daria Fokina, Pavel Toktaliev, Oleg Iliev, Ivan V. Oseledets:
Machine learning methods for prediction of breakthrough curves in reactive porous media. CoRR abs/2301.04998 (2023) - Wenlong Liao, Benjamin Schäfer, Dalin Qin, Gonghao Zhang, Zhixian Wang, Zhe Yang:
An Explainable Framework for Machine learning-Based Reactive Power Optimization of Distribution Network. CoRR abs/2311.03863 (2023) - Dumitru Potop-Butucaru, Albert Cohen, Gordon D. Plotkin, Hugo Pompougnac:
Bidirectional Reactive Programming for Machine Learning. CoRR abs/2311.16977 (2023) - Runze Mao, Yingrui Wang, Min Zhang, Han Li, Jiayang Xu, Xinyu Dong, Yan Zhang, Zhi X. Chen:
An integrated framework for accelerating reactive flow simulation using GPU and machine learning models. CoRR abs/2312.13513 (2023) - 2022
- Kotaro Yasui, Shunsuke Takano, Takeshi Kano, Akio Ishiguro:
Simple Reactive Head Motion Control Enhances Adaptability to Rough Terrain in Centipede Walking. Living Machines 2022: 262-266 - Andrea Apicella, Pasquale Arpaia, Andrea Cataldo, Egidio De Benedetto, Nicola Donato, Luigi Duraccio, Salvatore Giugliano, Roberto Prevete:
Adoption of Machine Learning Techniques to Enhance Classification Performance in Reactive Brain-Computer Interfaces. MeMeA 2022: 1-5 - Bjarno Oeyen, Joeri De Koster, Wolfgang De Meuter:
Reactive Programming on the Bare Metal: A Formal Model for a Low-Level Reactive Virtual Machine. REBLS@SPLASH 2022: 50-62 - Óscar Belmonte Fernández, Emilio Sansano-Sansano, Sergio Trilles, Antonio Caballer Miedes:
A Reactive Architectural Proposal for Fog/Edge Computing in the Internet of Things Paradigm with Application in Deep Learning. Artificial Intelligence, Machine Learning, and Optimization Tools for Smart Cities 2022: 155-175 - Stefan Bosse:
JAM: The JavaScript Agent Machine for Distributed Computing and Simulation with reactive and mobile Multi-agent Systems - A Technical Report. CoRR abs/2207.11300 (2022) - Mathias Schreiner, Arghya Bhowmik, Tejs Vegge, Jonas Busk, Ole Winther:
Transition1x - a Dataset for Building Generalizable Reactive Machine Learning Potentials. CoRR abs/2207.12858 (2022) - 2021
- Philippe Chrétienne:
Reactive and proactive single-machine scheduling to maintain a maximum number of starting times. Ann. Oper. Res. 298(1): 111-124 (2021) - Ayoub Tighazoui, Christophe Sauvey, Nathalie Sauer:
Predictive-reactive strategy for identical parallel machine rescheduling. Comput. Oper. Res. 134: 105372 (2021) - Ryo Yonemoto, Haruhiko Suwa:
Reactive Scheduling Based on Adaptive Manipulator Operations in a Job Shop Configuration with Two Machines. Int. J. Autom. Technol. 15(5): 661-668 (2021) - B. Ahmmed, Maruti Kumar Mudunuru, Satish Karra, S. C. James, Velimir V. Vesselinov:
A comparative study of machine learning models for predicting the state of reactive mixing. J. Comput. Phys. 432: 110147 (2021) - Imen Ben Fraj, Yousra Bendaly Hlaoui, Leila Ben Ayed:
A reactive system for specifying and running flexible cloud service business processes based on machine learning. COMPSAC 2021: 1483-1489 - Saleh O. Edhah, Ahmed Al-Durra:
Nonlinear Backstepping Control Design for stabilizing Synchronous Machine-Infinite Bus System using Reactive Power Compensation Device. IAS 2021: 1-8 - Pavel Gavrilenko, Bernard Haasdonk, Oleg Iliev, Mario Ohlberger, Felix Schindler, Pavel Toktaliev, Tizian Wenzel, Maha Youssef:
A Full Order, Reduced Order and Machine Learning Model Pipeline for Efficient Prediction of Reactive Flows. LSSC 2021: 378-386 - Pavel Gavrilenko, Bernard Haasdonk, Oleg Iliev, Mario Ohlberger, Felix Schindler, Pavel Toktaliev, Tizian Wenzel, Maha Youssef:
A full order, reduced order and machine learning model pipeline for efficient prediction of reactive flows. CoRR abs/2104.02800 (2021) - Shibabrat Naik, Vladimír Krajnák, Stephen Wiggins:
Support vector machines for learning reactive islands. CoRR abs/2107.08429 (2021) - Julian Arnold, Juan Carlos San Vicente Veliz, Debasish Koner, Narendra Singh, Raymond J. Bemish, Markus Meuwly:
Machine Learning Product State Distributions from Initial Reactant States for a Reactive Atom-Diatom Collision System. CoRR abs/2111.03563 (2021) - 2020
- Mana Jalali, Vassilis Kekatos, Nikolaos Gatsis, Deepjyoti Deka:
Designing Reactive Power Control Rules for Smart Inverters Using Support Vector Machines. IEEE Trans. Smart Grid 11(2): 1759-1770 (2020) - B. Ahmmed, Maruti Kumar Mudunuru, Satish Karra, S. C. James, Velimir V. Vesselinov:
A Comparative Study of Machine Learning Models for Predicting the State of Reactive Mixing. CoRR abs/2002.11511 (2020)
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