TY - GEN
T1 - Constrained image generation using binarized neural networks with decision procedures
AU - Korneev, Svyatoslav
AU - Narodytska, Nina
AU - Pulina, Luca
AU - Tacchella, Armando
AU - Bjorner, Nikolaj
AU - Sagiv, Mooly
N1 - Publisher Copyright: © Springer International Publishing AG, part of Springer Nature 2018.
PY - 2018
Y1 - 2018
N2 - We consider the problem of binary image generation with given properties. This problem arises in a number of practical applications, including generation of artificial porous medium for an electrode of lithium-ion batteries, for composed materials, etc. A generated image represents a porous medium and, as such, it is subject to two sets of constraints: topological constraints on the structure and process constraints on the physical process over this structure. To perform image generation we need to define a mapping from a porous medium to its physical process parameters. For a given geometry of a porous medium, this mapping can be done by solving a partial differential equation (PDE). However, embedding a PDE solver into the search procedure is computationally expensive. We use a binarized neural network to approximate a PDE solver. This allows us to encode the entire problem as a logical formula. Our main contribution is that, for the first time, we show that this problem can be tackled using decision procedures. Our experiments show that our model is able to produce random constrained images that satisfy both topological and process constraints.
AB - We consider the problem of binary image generation with given properties. This problem arises in a number of practical applications, including generation of artificial porous medium for an electrode of lithium-ion batteries, for composed materials, etc. A generated image represents a porous medium and, as such, it is subject to two sets of constraints: topological constraints on the structure and process constraints on the physical process over this structure. To perform image generation we need to define a mapping from a porous medium to its physical process parameters. For a given geometry of a porous medium, this mapping can be done by solving a partial differential equation (PDE). However, embedding a PDE solver into the search procedure is computationally expensive. We use a binarized neural network to approximate a PDE solver. This allows us to encode the entire problem as a logical formula. Our main contribution is that, for the first time, we show that this problem can be tackled using decision procedures. Our experiments show that our model is able to produce random constrained images that satisfy both topological and process constraints.
UR - http://www.scopus.com/inward/record.url?scp=85049675281&partnerID=8YFLogxK
U2 - https://doi.org/10.1007/978-3-319-94144-8_27
DO - https://doi.org/10.1007/978-3-319-94144-8_27
M3 - منشور من مؤتمر
SN - 9783319941431
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 438
EP - 449
BT - Theory and Applications of Satisfiability Testing – SAT 2018 - 21st International Conference, SAT 2018, Held as Part of the Federated Logic Conference, FloC 2018, Proceedings
A2 - Beyersdorff, Olaf
A2 - Wintersteiger, Christoph M.
T2 - 21st International Conference on Theory and Applications of Satisfiability Testing, SAT 2018 Held as Part of the Federated Logic Conference, FloC 2018
Y2 - 9 July 2018 through 12 July 2018
ER -
