Mark Fuge

1. Deep learning for molecular design—a review of the state of the art
   2019/01/01 by Daniel C. Elton, Zois Boukouvalas, Mark Fuge +2 · 1 voice · 9 citations
   Computer Science · Environmental Science · Materials Science · Mathematics · Physics and Astronomy · #Chemistry and Chemical Engineering #Computational Drug Discovery Methods #Machine Learning in Materials Science #cs.LG #physics.chem-ph #stat.ML
2. Airfoil Design Parameterization and Optimization using Bézier Generative Adversarial Networks
   2020/06/21 by Wei Chen, Kevin Chiu, Chen, Wei +3 · 9 citations
   Computer Science · Physics and Astronomy · #Advanced Multi-Objective Optimization Algorithms #Computational Engineering #FOS: Computer and information sciences #Finance #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks #and Science (cs.CE)
3. Formulating or Fixating: Effects of Examples on Problem Solving Vary as a Function of Example Presentation Interface Design
   2024/01/19 by Joel Chan, Chan, Joel, Zijian Ding +5 · 1 citation
   Psychology · #Educational Games and Gamification #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Team Dynamics and Performance #Visual and Cognitive Learning Processes
4. EngiBench: A Framework for Data-Driven Engineering Design Research
   2025/06/02 by Florian Felten, Felten, Florian, Gabriel Apaza +20 · 2 citations
   Computer Science · Materials Science · Physics and Astronomy · #Advanced Multi-Objective Optimization Algorithms #Machine Learning in Materials Science #Model Reduction and Neural Networks
5. Adaptive Expansion Bayesian Optimization for Unbounded Global Optimization
   2020/01/12 by Wei Chen, Mark Fuge, Chen, Wei +1 · 1 citation
   Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Advanced Multi-Objective Optimization Algorithms #FOS: Computer and information sciences #FOS: Mathematics #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Optimization and Control (math.OC)