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GE Research “Industrial Applications of Intelligent Adaptive Sampling Methods for Multi- Objective Optimization.” Design Engineering & 
 Manufacturing, 2019. Link. Ph.D. “Adaptive simulation selection for the
discovery of the ground state line of
binary alloys with a limited
computational budget.” Chapter in:
 Recent Progress and Modern Challenges in 
 Applied Mathematics, Modeling and 
 Computational Science,
 Springer-Verlag, 2017. Link. “Quantifying uncertainties in first-principles alloy thermodynamics using cluster expansions.”
Journal of Computational Physics, 2016.
Link. “Predicting low-thermal-conductivity Si-Ge nanowires with a modified
 cluster expansion method.” Physical Review B, 2015. PDF (free) ©2015 American Physical Society “Bayesian uncertainty quantification in
 the evaluation of alloy properties with the cluster expansion method.” Computer Physics Communications,
 2014. Special Edition. Link. 
(3) “Relative entropy as model selection 
 tool in cluster expansions.”
 Physical Review B, 2013. PDF (free) ©2013 American Physical Society 
Undergraduate (2) “Hydronium-dominated ion transport 
 in carbon-dioxide-saturated
 electrolytes at low salt concentrations
 in nanochannels.”
 Physical Review E, 2011. PDF (free) ©2011 American Physical Society “Low-loss polarization-maintaining 
 fusion splicing of single-mode fibers and hollow-core photonic crystal fibers, relevant for monolithic fiber laser pulse compression.”
 Optics Express, 2009. Link. https://www.intechopen.com/online-first/industrial-applications-of-intelligent-adaptive-sampling-methods-for-multi-objective-optimizationhttps://link.springer.com/chapter/10.1007/978-1-4939-6969-2_6http://www.sciencedirect.com/science/article/pii/S0021999116302972Publications_files/2015Kristensen_nanowires.pdfhttp://www.sciencedirect.com/science/article/pii/S0010465514002562Publications_files/2013Kristensen_RelEnt.pdfPublications_files/2011Kristensen_Hydronium.pdfhttps://www.osapublishing.org/oe/abstract.cfm?uri=oe-16-13-9986shapeimage_2_link_0shapeimage_2_link_1shapeimage_2_link_2shapeimage_2_link_3shapeimage_2_link_4shapeimage_2_link_5shapeimage_2_link_6shapeimage_2_link_7
GE Research (17) University of Connecticut, CT “Recent Progress in Black-Box
 Function Optimization for
 Industry Problems” January 2019 (invited) (16) GE GRC, NY
 Thermosciences - lunch & learn “Bayesian Hybrid Modeling and Intelligent Design and Analysis of
 Computer Experiments for 
 Optimization” October 2018 (invited) (15) ASME IDETC/CIE, Quebec City, Canada August 2018. “Polynomial Representation of the
 Gaussian Process”.
 Paper ID: DETC2018-85145. (14) AIAA Aviation and Aeronautics
 Forum and Exposition, GA, US June 2018.
 “Efficient robust design
 optimization using Gaussian
 process and intelligent sampling”. (13) IGTI ASME, Oslo, NO June 2018. “A Gaussian Process Modeling
 Approach for Fast Robust Design
 with Uncertain Inputs”. Link, Paper ID: TE2018-77007 (12) ASME V&V, Minneapolis, MN, US
 May 2018 “Optimal Information Acquisition
 Algorithms for Inferring the Order
 of Sensitivity Indices” VVS2018-9350 Link to conference AIAA SciTech Forum, FL, US 
Talk 1: “Bayesian Multi-Source Modeling with 
 Legacy Data.” Talk 2: “Intelligent sampling
 framework for 
 multi-objective optimization in high 
 dimensional design space.” (10) ASME V&V, Las Vegas, NV, US 
 “Heteroskedastic Gaussian
 Process Meta-models for Improved
 Predictive Uncertainties.” conference. the talk (p. 19). MODSIM World, Virginia Beach, VA, US
Analytics and decision making.
conference.
paper. ASME IDETC/CIE, Charlotte, NC, US conference.
 paper. Ph.D. (7) TMS (144th), Orlando, FL, US conference. PDF of talk 1. PDF of talk 2. (6) University of Warwick, UK Warwick centre for predictive 
 modeling Seminar (invited) PDF of talk. (5) University of Florida, FL, US (invited) MSE Department conference. (4) TMS (143rd), San Diego, CA, US conference. (3) SIAM on UQ 2014, Savannah,
 Georgia, US conference. download presentation. 
(2) USNCCM (12th), Raleigh, NC, US conference. 
(1) TMS (142nd), San Antonio, TX, US
 (invited) conference. download presentation. https://www.asme.org/events/turbo-expo/program#/track/detail/184https://www.asme.org/events/vandv/program#/track/detail/29https://www.asme.org/events/vandvhttps://www.asme.org/wwwasmeorg/media/ResourceFiles/Events/VandV/V-V2017_Program_compress.pdfhttp://www.modsimworld.org/http://www.modsimworld.org/papers/2017/Predictive_analytics_with_an_advanced_Bayesian_modeling_framework.pdfhttps://www.asme.org/events/idetcciehttp://proceedings.asmedigitalcollection.asme.org/proceeding.aspx?articleid=2591651http://www.tms.org/meetings/annual-15/AM15home.aspxPublications_files/TMS2015_1_JesperKristensen.pdfPublications_files/TMS_2_nanowire.pdfPublications_files/2015_Seminar_Warwick_JesperKristensen.pdfhttp://www.mse.ufl.edu/http://www.tms.org/meetings/annual-14/AM14home.aspxhttp://www.siam.org/meetings/uq14/Publications_files/2_2014_SIAM_UQ.pdfhttp://12.usnccm.org/http://www.tms.org/meetings/annual-13/AM13home.aspxPublications_files/1_2013_TMS.pdfshapeimage_4_link_0shapeimage_4_link_1shapeimage_4_link_2shapeimage_4_link_3shapeimage_4_link_4shapeimage_4_link_5shapeimage_4_link_6shapeimage_4_link_7shapeimage_4_link_8shapeimage_4_link_9shapeimage_4_link_10shapeimage_4_link_11shapeimage_4_link_12shapeimage_4_link_13shapeimage_4_link_14shapeimage_4_link_15shapeimage_4_link_16shapeimage_4_link_17shapeimage_4_link_18

Jesper Toft Kristensen

Journal Publications & Book Chapters
Talks

For my full citations list: Link to Citations Profile

GE Technical Reports are internal publications that undergo peer review from senior technical staff and management. GE Research “Finding Maximum Expected Improvement for High-Dimensional Design Optimization” AIAA Aviation and Aeronautics Forum and Exposition, 2019. (to be held June 2019, Dallas, TX) (16) “Bayesian Hybrid Modeling with
 Synedix”
 GE GRC Manual (52 pages), 2019. (15) “Intelligent Design and Analysis of
 Computer Experiments with
 Synedix”
 GE GRC Manual (41 pages), 2019. (14) “Legacy Data for New Design” Patent filed with GE GRC, 2018. (13) “Advanced Bayesian Probabilistic
 Methods for Compressor Modeling” GE technical report, 2018. (12) “Efficient Robust Design Optimization
 using Gaussian Process and 
 Intelligent Sampling” 2018 Multidisciplinary Analysis &
 Optimization Conference. (11) “Polynomial Representation of the 
 Gaussian Process” ASME IDETC/CIE 2018. (10) “A Gaussian Process Modeling
 Approach for Fast Robust Design
 with Uncertain Inputs” ASME IGTI, 2018
 (paper ID: GT2018-77007) (9) “Bayesian multi-source modeling with
 legacy data” GE technical report, 2018. (8) “An intelligent sampling framework for
 multi-objective optimization in high-
 dimensional design space”
 SciTech, 2018. “A Gaussian process modeling
approach for fast robust design with uncertain inputs”
GE technical report, 2017. (6) “Increasing the efficiency and accuracy
 of Bayesian Hybrid Modeling with
 Adaptive Sequential Monte Carlo
 (ASMC).”
 GE technical report, 2017. (5) “Portable Bayesian Hybrid Modeling”
 GE technical report, 2017. (4) “Documentation of the GE Piston GUI
 and Transient Calibration of Large
 Data Sets”
 GE technical report, 2017. (3) “Predictive analytics with an advanced 
 Bayesian modeling framework”
 MODSIM World, 2017 Link. (2) “Modern Probabilistic Methods for
 Pump Predictability and Optimization at the System and Component
 Levels” GE technical report, 2016. (1) “Expected-Improvement Based
 Methods for Adaptive Sampling in
 Multi-Objective Optimization
 Problems”
 ASME IDETC/CIE, 2016. Link. http://www.modsimworld.org/papers/2017/Predictive_analytics_with_an_advanced_Bayesian_modeling_framework.pdfhttp://proceedings.asmedigitalcollection.asme.org/proceeding.aspx?articleid=2591651shapeimage_11_link_0shapeimage_11_link_1
Conference Papers,
GE Technical Reports, & Patents