@inproceedings {pub4072,
	title = {Learning Time-series Data of Industrial Design Optimization using Recurrent Neural Networks},
	author = {Sneha Saha AND Thiago de Jesus de Araujo Rios AND Stefan Menzel AND Bernhard Sendhoff AND Thomas B{\"a}ck AND Xin Yao AND Zhao Xu AND Patricia Wollstadt},
	year = {2019},
	month = {November},
	abstract = {In automotive digital development, 3D shape morphing techniques are used to create new designs in order to match design  targets,  such  as  aerodynamic  or  stylistic  requirements. Control-point  based  shape  morphing  alters  existing  geometries either through human user interactions or through computational optimization  algorithms  that  optimize  for  product  performance targets.  Shape  morphing  is  typically  continuous  and  results  in potentially  large  data  sets  of  time-series  recordings  of  control point  movements.  In  the  present  paper,  we  utilize  recurrent neural  networks  to  model  such  time-series  recordings  in  order to  predict  future  design  steps  based  on  the  history  of  currently performed  design  modifications.  To  build  a  data  set  sufficiently large   for   training   of   neural   networks,   we   use   target   shape matching   optimization   as   digital   analogy   for   a   human   user interactive  shape  modification  and  to  build  data  sets  of  control point movements in an automated fashion. Experiments show the potential of recurrent neural networks to successfully learn time-series  data  representing  design  changes  and  to  perform  single- and  multi-step  prediction  of  potential  next  design  steps.  We thus demonstrate the feasibility of recurrent neural networks for learning successful design sequences in order to predict promising next  design  steps  in  future  design  tasks.},
	publisher = {IEEE},
	booktitle = {2019 ICDM Workshop: Learning and Mining with Industrial Data (LMID)}
}