Design optimization of dynamic flexible multibody systems using the discrete adjoint variable method
The design space of dynamic multibody systems (MBSs), particularly those with flexible components, is considerably large. Consequently, having a means to efficiently explore this space and find...
A low order, torsion deformable spatial beam element based on the absolute nodal coordinate formulation and Bishop frame
Heretofore, the Serret-Frenet frame has been the ubiquitous choice for analyzing the elastic deformations of beam elements. It is well known that this frame is undefined at the inflection points...
Optimal Design of Continuum Robots with Reachability Constraints
While multi-joint continuum robots are highly dexterous and flexible, designing an optimal robot can be challenging due to its kinematics involving curvatures. Hence, the current work presents a...
Optimal design of frame structures with mixed categorical and continuous design variables using the Gumbel-Softmax method
In optimizing real-world structures, due to fabrication or budgetary restraints, the design variables may be restricted to a set of standard engineering choices. Such variables, commonly called...
A hyperreduced reduced basis element method for reduced-order modeling of component-based nonlinear systems
We introduce a hyperreduced reduced basis element method for model reduction of parameterized, component-based systems in continuum mechanics governed by nonlinear partial differential equations....
Configuration Design of Mechanical Assemblies using an Estimation of Distribution Algorithm and Constraint Programming
A configuration design problem in mechanical engineering involves finding an optimal assembly of components and joints that realizes some desired performance criteria. Such a problem is a...
EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning
Over the last few years, deep learning techniques have yielded significant improvements in image inpainting. However, many of these techniques fail to reconstruct reasonable structures as they are...
Single MR Image Super-Resolution using Generative Adversarial Network
Spatial resolution of medical images can be improved using super-resolution methods. Real Enhanced Super Resolution Generative Adversarial Network (Real-ESRGAN) is one of the recent effective...
Multi-Modality Image Super-Resolution using Generative Adversarial Networks
Over the past few years deep learning-based techniques such as Generative Adversarial Networks (GANs) have significantly improved solutions to image super-resolution and image-to-image translation...
Two-Stage Convolutional Neural Network for Breast Cancer Histology Image Classification
This paper explores the problem of breast tissue classification of microscopy images. Based on the predominant cancer type the goal is to classify images into four categories of normal, benign, in...
Edge-Informed Single Image Super-Resolution
The recent increase in the extensive use of digital imaging technologies has brought with it a simultaneous demand for higher-resolution images. We develop a novel edge-informed approach to single...
Image Colorization with Generative Adversarial Networks
Over the last decade, the process of automatic image colorization has been of significant interest for several application areas including restoration of aged or degraded images. This problem is...
Automatic Temporally Coherent Video Colorization
Greyscale image colorization for applications in image restoration has seen significant improvements in recent years. Many of these techniques that use learning-based methods struggle to...
Artist-Guided Semiautomatic Animation Colorization
There is a delicate balance between automating repetitive work in creative domains while staying true to an artist's vision. The animation industry regularly outsources large animation workloads...
Multi-Modality Image Inpainting using Generative Adversarial Networks
Deep learning techniques, especially Generative Adversarial Networks (GANs) have significantly improved image inpainting and image-to-image translation tasks over the past few years. To the best...
Deep Learning-Based MR Image Re-parameterization
Magnetic resonance (MR) image re-parameterization refers to the process of generating via simulations of an MR image with a new set of MRI scanning parameters. Different parameter values generate...
Characterizing a Configuration Where Inference-Time PRM-Pruned Fragment Grafting Is Inert: Evidence from Three Reasoning LMs
Diversity collapse in parallel chain-of-thought has motivated inference-time interventions built on a natural design: when a process reward model (PRM) prunes a chain, its high-PRM prefix is...