25 papers · ranked by Valyu relevance
Shikun Chen, Zebin Huang, Wenlong Zheng, Yuanchao Liu
Mathematical optimization is fundamental across many scientific and engineering applications. While data-driven models like gradient boosting and random forests excel at prediction tasks, they often lack mathematical regularity, being non-differentiable or even discontinuous. These models are commonly used to predict…
Yifan Wu, Aron Walsh, Alex Ganose
What is the minimum number of experiments, or calculations, required to find an optimal solution? Relevant chemical problems range from identifying a compound with target functionality within a given phase space to controlling materials synthesis and device fabrication conditions. A common feature in this application…
Xuzhen He, Viacheslav Kovtun
The recent dramatic progress in machine learning is partially attributed to the availability of high-performant computers and development tools. The accelerated linear algebra (XLA) compiler is one such tool that automatically optimises array operations (mostly fusion to reduce memory operations) and compiles the…
Rahib H. Abiyev, Mustafa Tunay
A novel learning algorithm for solving global numerical optimization problems is proposed. The proposed learning algorithm is intense stochastic search method which is based on evaluation and optimization of a hypercube and is called the hypercube optimization (HO) algorithm. The HO algorithm comprises the…
Desmond J. Higham
I give a brief, non-technical, historical perspective on numerical analysis and optimization. I also touch on emerging trends and future challenges. This content is based on the short presentation that I made at the opening ceremony of The International Conference on Numerical Analysis and Optimization, which was held…
Santoshi Subhalaxmi Ray, Manideepa Saha
Unconstrained convex optimization problems have enormous applications in various field of science and engineering. Different iterative methods are available in literature to solve such problem, and Newton method is among the oldest and simplest one. Due to slow convergence rate of Newton's methods, many research have…
Guanglu Zhang, Qihang Shan, Jonathan Cagan
This paper introduces a GPU-based complete search method to enclose the global minimum of a nonlinear function subject to simple bounds on the variables. Using interval analysis, coupled with the computational power and architecture of GPU, the method iteratively rules out the regions in the search domain where the…
Cameron Meaney, Mohammad Kohandel, Arian Novruzi
External beam radiation therapy is a key part of modern cancer treatments which uses high doses of radiation to destroy tumour cells. Despite its widespread usage and extensive study in theoretical, experimental, and clinical works, many questions still remain about how best to administer it. Many mathematical studies…
Stephan Grein, David R. Penas, Daniel Weindl, Polina Lakrisenko + 2 more
Dynamic models are central to the computational life sciences but typically contain unknown parameters that must be inferred from experimental data. High-throughput measurements have made this task increasingly challenging, yielding high-dimensional search spaces and non-convex objectives with many local optima. This…
Anugrah Jo Joshy, John T. Hwang
algorithms Authors: ['Anugrah Jo Joshy' 'John T. Hwang'] Applications of numerical optimization have appeared across a broad range of research fields, from finance and economics to the natural sciences and engineering. It is well known that the optimization techniques employed in each field are specialized to suit…
Rahul Bhadani
Often in physical science research, we end up with a hard problem of optimizing a function (called objective) that satisfies a range of constraints - linear or non-linear equalities and inequalities. The optimizers usually also have to adhere to the upper and lower bound. We recently worked on a similar problem in…
Saeed Asadi, Sonia Gharibzadeh, Shiva Zangeneh, Masoud Reihanifar + 2 more
Multidimensional Surface 3D Visualizations and Initial Point Sensitivity Authors: ['Saeed Asadi' 'Sonia Gharibzadeh' 'Shiva Zangeneh' 'Masoud Reihanifar' 'Mehrzad Rahimi' 'Lazim Abdullah'] This study examines several renowned gradient-based optimization techniques and focuses on their computational efficiency and…
Matt J. Owen, Gary R. Mirams
Ion channel models present many challenging optimisation problems. These include unidentifiable parame- ters, noisy data, unobserved states, and a combination of both fast and slow timescales. This can make it difficult to choose a suitable optimisation routine a priori. Nevertheless, many attempts have been made to…
Riley Hickman, Priyansh Parakh, Austin Cheng, Qianxiang Ai + 3 more
Experiment planning algorithms are a required component of autonomous platforms for scientific discovery. Selecting a suitable optimization algorithm for a novel application is an important yet difficult choice a researcher has to make based on past empirical performance on similar tasks. To facilitate the evaluation…
Fabian Fröhlich, Peter K. Sorger
Ordinary differential equation (ODE) models are widely used to describe biochemical processes, since they effectively represent mass action kinetics. Optimization-based calibration of ODE models on experimental data can be challenging, even for low-dimensional problems. However, reliable model calibration is a…
Boon Xian Chai, Boris Eisenbart, Mostafa Nikzad, Bronwyn Fox + 12 more
'Yuqi Wang' 'Kyaw Hlaing Bwar' 'Kaiyu Zhang' 'Marcin Sosnowski' 'Jaroslaw Krzywanski' 'Karolina Grabowska' 'Dorian Skrobek' 'Ghulam Moeen Uddin' 'Yunfei Gao' 'Anna Zylka' 'Anna Kulakowska' 'Bachil El Fil'] The utilisation of numerical process simulation has greatly facilitated the challenging task of liquid composite…
Daniel Gaissmaier, Matthias van den Borg, Donato Fantauzzi, Timo Jacob
In this work, we demonstrate the superior exploration capabilities of the population-based methods over the sequential one-parameter parabolic interpolation (SOPPI) approach to optimise ReaxFF force field parameters. Evolutionary algorithms (EAs) are heuristic-based approaches using a population of concurrent models in…
Carlos Vilas, Eva Balsa-Canto, Maria-Sonia G García, Julio R Banga + 1 more
'Antonio A Alonso'] Background Systems biology allows the analysis of biological systems behavior under different conditions through in silico experimentation. The possibility of perturbing biological systems in different manners calls for the design of perturbations to achieve particular goals. Examples would include…
Mohammed Baragilly, Brian H Willis
Meta-analysis may be used to summarise a test’s accuracy. Often the sensitivity and specificity are the measures of interest and as these are correlated a bivariate random effects model is commonly used to fit the data. This model has five parameters and it may be optimised using a Newton-Raphson based algorithm…
Xin‐She Yang, Sławomir Kozieł, Leifur Leifsson
Modelling, simulation and optimization form an integrated part of modern design practice in engineering and industry. Tremendous progress has been observed for all three components over the last few decades. However, many challenging issues remain unresolved, and the current trends tend to use nature-inspired…
Dhananjay Kumar, Chaman Kumar, Ankit Kumar, Chinmaya Sahoo + 7 more
Introduction This study aimed to optimize the process parameters for developing nutritionally enriched biscuits incorporating Moringa oleifera leaf powder (MOLP) and Artocarpus heterophyllus (jackfruit) seed powder using Response Surface Methodology (RSM). Methods A Box-Behnken design was employed to evaluate the…
Authors not listed
As a prove of concept for experimental geochemistry, an advanced 3D numerical framework, here and after called Digital Twin (DT), of a diffusion experiment conducted at a synchrotron beamline, has been implemented using in-situ measurements data, physics-based modelling, a machine learning (ML) model, and parameter…
Riley Hickman, Matteo Aldeghi, Alán Aspuru-Guzik
Model-based optimization strategies, such as Bayesian optimization (BO), have been deployed across the natural sciences in design and discovery campaigns due to their sample efficiency and flexibility. The combination of such strategies with automated laboratory equipment and/or high-performance computing in a…
Authors not listed
Accurate electronic structure simulations of strongly correlated metal oxides are crucial for the atomic level understanding of heterogeneous catalysts, batteries and photovoltaics; but remain challenging to perform in a computationally tractable manner. Hubbard corrected density functional theory (DFT+U) in a…
Alexander L. Bowler, Nasser Alkhulaifi, Sarah Bowler, Joanna H. Sier + 5 more
Food production is a significant contributor to global greenhouse gas emissions and deforestation, exacerbated by substantial food waste. Converting food waste into yeast protein offers a sustainable solution to enhance food security and contribute to a circular economy. However, due to the diverse and variable nature…