Building surrogate models for engineering problems by integrating limited simulation data and monotonic engineering knowledge

[1]  Shigeru Obayashi,et al.  Updating Kriging Surrogate Models Based on the Hypervolume Indicator in Multi-Objective Optimization , 2013 .

[2]  Risto Miikkulainen,et al.  Efficient evolution of neural network topologies , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[3]  Yan Yan,et al.  Design optimization by integrating limited simulation data and shape engineering knowledge with Bayesian optimization (BO-DK4DO) , 2020, J. Intell. Manuf..

[4]  C. Shoemaker,et al.  Combining radial basis function surrogates and dynamic coordinate search in high-dimensional expensive black-box optimization , 2013 .

[5]  Farrokh Mistree,et al.  A Goal-Oriented, Inverse Decision-Based Design Method to Achieve the Vertical and Horizontal Integration of Models in a Hot Rod Rolling Process Chain , 2017, DAC 2017.

[6]  Sankaran Mahadevan,et al.  A Single-Loop Kriging Surrogate Modeling for Time-Dependent Reliability Analysis , 2016 .

[7]  Aimin Zhou,et al.  A Multioperator Search Strategy Based on Cheap Surrogate Models for Evolutionary Optimization , 2015, IEEE Transactions on Evolutionary Computation.

[8]  Yaser S. Abu-Mostafa,et al.  Learning from hints in neural networks , 1990, J. Complex..

[9]  T. J. Mitchell,et al.  Bayesian design and analysis of computer experiments: Use of derivatives in surface prediction , 1993 .

[10]  Donald E. Grierson,et al.  Comparison among five evolutionary-based optimization algorithms , 2005, Adv. Eng. Informatics.

[11]  Jude W. Shavlik,et al.  Knowledge-Based Artificial Neural Networks , 1994, Artif. Intell..

[12]  Carl E. Rasmussen,et al.  In Advances in Neural Information Processing Systems , 2011 .

[13]  Ferruh Öztürk,et al.  Intelligent die design optimization using enhanced differential evolution and response surface methodology , 2015, J. Intell. Manuf..

[14]  Rammohan Mallipeddi,et al.  An evolving surrogate model-based differential evolution algorithm , 2015, Appl. Soft Comput..

[15]  Carolyn Conner Seepersad,et al.  Building Surrogate Models Based on Detailed and Approximate , 2004, DAC 2004.

[16]  Farrokh Mistree,et al.  Kriging Models for Global Approximation in Simulation-Based Multidisciplinary Design Optimization , 2001 .

[17]  Aki Vehtari,et al.  Gaussian processes with monotonicity information , 2010, AISTATS.

[18]  Gianfranco La Rocca,et al.  Knowledge based engineering: Between AI and CAD. Review of a language based technology to support engineering design , 2012, Adv. Eng. Informatics.

[19]  Raphael T. Haftka,et al.  On the Use of Symmetries in Building Surrogate Models , 2019, Journal of Mechanical Design.

[20]  Joseph Sill,et al.  Monotonic Networks , 1997, NIPS.

[21]  T. Simpson,et al.  Comparative studies of metamodelling techniques under multiple modelling criteria , 2001 .

[22]  Kalyanmoy Deb,et al.  A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II , 2000, PPSN.

[23]  Marina Velikova,et al.  Monotone and Partially Monotone Neural Networks , 2010, IEEE Transactions on Neural Networks.

[24]  Miguel-Ángel Sicilia Ontology of systems and software engineering , 2007, Adv. Eng. Informatics.

[25]  Liu Yang,et al.  B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data , 2020, J. Comput. Phys..

[26]  Kyung K. Choi,et al.  Conservative Surrogate Model Using Weighted Kriging Variance for Sampling-Based RBDO , 2013, Journal of Mechanical Design.

[27]  Farrokh Mistree,et al.  A rule-based method for automated surrogate model selection , 2020, Adv. Eng. Informatics.

[28]  A. M. M. Sharif Ullah,et al.  Modeling and simulation of complex manufacturing phenomena using sensor signals from the perspective of Industry 4.0 , 2019, Adv. Eng. Informatics.