3 citations · 7 across the 8 of their papers we have counts for
9 papers
A Mesh Is Worth 512 Numbers: Spectral-domain Diffusion Modeling for High-dimension Shape Generation
Jiajie Fan, Amal Trigui, Andrea Bonfanti +3
Recent advancements in learning latent codes derived from high-dimensional shapes have demonstrated impressive outcomes in 3D generative modeling. Traditionally, these approaches e…
Evolving Hard Maximum Cut Instances for Quantum Approximate Optimization Algorithms
Shuaiqun Pan, Yash J. Patel, Aneta Neumann +3
Variational quantum algorithms, such as the Recursive Quantum Approximate Optimization Algorithm (RQAOA), have become increasingly popular, offering promising avenues for employing…
Abnormal Mutations: Evolution Strategies Don't Require Gaussianity
Jacob de Nobel, Diederick Vermetten, Hao Wang +3
The mutation process in evolution strategies has been interlinked with the normal distribution since its inception. Many lines of reasoning have been given for this strong dependen…
Transfer Learning of Surrogate Models: Integrating Domain Warping and Affine Transformations
Shuaiqun Pan, Diederick Vermetten, Manuel López-Ibáñez +2
Surrogate models provide efficient alternatives to computationally demanding real world processes but often require large datasets for effective training. A promising solution to t…
Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks
Shuaiqun Pan, Diederick Vermetten, Manuel López-Ibáñez +2
Surrogate models are frequently employed as efficient substitutes for the costly execution of real-world processes. However, constructing a high-quality surrogate model often deman…
NeuroNURBS: Learning Efficient Surface Representations for 3D Solids
Jiajie Fan, Babak Gholami, Thomas Bäck +1
Boundary Representation (B-Rep) is the de facto representation of 3D solids in Computer-Aided Design (CAD). B-Rep solids are defined with a set of NURBS (Non-Uniform Rational B-Spl…
Fréchet Denoised Distance: Enhancing Plausibility Evaluation for Generated Designs with Denoising Autoencoder
Jiajie Fan, Amal Trigui, Thomas Bäck +1
A great interest has arisen in using Deep Generative Models (DGM) for generative design. When assessing the quality of the generated designs, human designers focus more on structur…
Large-scale Benchmarking of Metaphor-based Optimization Heuristics
Diederick Vermetten, Carola Doerr, Hao Wang +2
The number of proposed iterative optimization heuristics is growing steadily, and with this growth, there have been many points of discussion within the wider community. One partic…
On the Noise Scheduling for Generating Plausible Designs with Diffusion Models
Jiajie Fan, Laure Vuaille, Thomas Bäck +1
Deep Generative Models (DGMs) are widely used to create innovative designs across multiple industries, ranging from fashion to the automotive sector. In addition to generating imag…