Results 101 to 110 of about 3,855 (254)

Hierarchical Differentiable Fluid Simulation

Computer Graphics Forum, EarlyView.
We introduce a two‐step algorithm that significantly reduces memory usage for solving control problems using differentiable fluid simulation techniques: our method first optimizes for bulk forces at reduced resolution, then refines local details over sub‐domains while maintaining differentiability. In trading runtime for memory, it enables optimization
Xiangyu Kong, Arnaud Schoentgen, Damien Rioux‐Lavoie, Paul G. Kry, Derek Nowrouzezahrai   +4 more
wiley   +1 more source

Multi-objective optimisation with a sequence-based selection hyper-heuristic [PDF]

, 2016
Hyper-heuristics have been used widely to solve optimisation problems, often single-objective and discrete in nature. Herein, we extend a recently-proposed selection hyper-heuristic to the multiobjective domain and with it optimise continuous problems ...
Keedwell, EK, Walker, DJ
core   +1 more source

Adaptive Sampling for BRDF Acquisition

Computer Graphics Forum, EarlyView.
We propose a data‐driven adaptive sampling strategy that predicts the optimal sampling pattern and count for BRDF acquisition from a single image, reducing capture time while preserving quality. Abstract The bidirectional reflectance distribution function (BRDF) describes the ratio of incoming radiance to outgoing radiance for all possible pairs of ...
Behnaz Kavoosighafi, Saghi Hajisharif, Jonas Unger, Ehsan Miandji   +3 more
wiley   +1 more source

Student Surpasses the Teacher: Apprenticeship Learning for Quadratic Unconstrained Binary Optimisation

Algorithms
This study introduces a novel train-and-test approach referred to as apprenticeship learning (AL) for generating selection hyper-heuristics to solve the Quadratic Unconstrained Binary Optimisation (QUBO) problem.
Jack Cakebread, Warren G. Jackson, Daniel Karapetyan, Andrew J. Parkes, Ender Özcan   +4 more
doaj   +1 more source

Automated generation of constructive ordering heuristics for educational timetabling [PDF]

, 2017
Construction heuristics play an important role in solving combinatorial optimization problems. These heuristics are usually used to create an initial solution to the problem which is improved using optimization techniques such as metaheuristics.
B McCollum, EK Burke, EK Burke, EK Burke, Ender Özcan, J Branke, J Koza, JH Drake, M Bader-El-Den, M O’Neill, Nelishia Pillay, R Qu, RI McKay   +12 more
core   +2 more sources

Hyperion – A Recursive Hyper-Heuristic Framework [PDF]

, 2011
Hyper-heuristics are methodologies used to search the space of heuristics for solving computationally difficult problems. We describe an object-oriented domain analysis for hyper-heuristics that orthogonally decomposes the domain into generative policy components.
Jerry Swan, Ender Özcan, Graham Kendall
openaire   +1 more source

OUGS: Active View Selection via Object‐aware Uncertainty Estimation in 3DGS

Computer Graphics Forum, EarlyView.
Abstract Recent advances in 3D Gaussian Splatting (3DGS) have achieved state‐of‐the‐art results for novel view synthesis. However, efficiently capturing high‐fidelity reconstructions of specific objects within complex scenes remains a significant challenge.
Haiyi Li, Qi Chen, Denis Kalkofen, Hsiang‐Ting Chen   +3 more
wiley   +1 more source

Infection Aware Hyper-Heuristic Framework for Hospital Room–Patient Matching

Algorithms
The assignment of hospital rooms to patients is a critical operational decision that has a direct impact on patient safety, infection control, and staff workload.
Kassem Danach, Wael Hosny Fouad Aly, Chadi Fouad Riman   +2 more
doaj   +1 more source

Interpolated Adaptive Linear Reduced Order Modeling for Deformation Dynamics

Computer Graphics Forum, EarlyView.
Abstract Linear reduced‐order modeling (ROM) is widely used for efficient simulation of deformation dynamics, but its accuracy is often limited by the fixed linearization of the reduced mapping. We propose a new adaptive strategy for linear ROM that allows the reduced mapping to vary dynamically in response to the evolving deformation state ...
Y. Tao, M. Chiaramonte, P. Fernandez
wiley   +1 more source

A Bi-objective Hyper-Heuristic Support Vector Machines for Big Data Cyber-Security

IEEE Access, 2018
Cyber security in the context of big data is known to be a critical problem and presents a great challenge to the research community. Machine learning algorithms have been suggested as candidates for handling big data security problems.
Nasser R. Sabar, Xun Yi, Andy Song
doaj   +1 more source