2023/10/03 by Khaish Singh Chadha, Prabhat Kumar, Chadha, Khaish Singh +1
Computer Science · Engineering · Mathematics · #Adaptive optimization #Advanced Numerical Analysis Techniques #Algorithm #Architecture #Artificial intelligence #Artificial neural network #Computational Engineering #Computer engineering #Computer network #Computer science #Convolutional neural network #Encoder #Engineering #FOS: Computer and information sciences #Finance #Finite element method #Mathematics #Metaheuristic Optimization Algorithms Research #Minification #Network architecture #Network topology #Robustness (evolution) #Topology (electrical circuits) #Topology Optimization in Engineering #Topology optimization #and Science (cs.CE)
paper · pdf · doi:10.48550/arxiv.2310.02069
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2023/10/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper presents an adaptive convolutional neural network (CNN) architecture that can automate diverse topology optimization (TO) problems having different underlying physics. The architecture uses the encoder-decoder networks with dense layers in the middle which includes an additional adaptive layer to capture complex geometrical features. The network is trained using the dataset obtained from the three open-source TO codes involving different physics. The robustness and success of the presented adaptive CNN are demonstrated on compliance minimization problems with constant and design-dependent loads and material bulk modulus optimization. The architecture takes the user's input of the volume fraction. It instantly generates optimized designs resembling their counterparts obtained via open-source TO codes with negligible performance and volume fraction error.