Search · four archives
Search · four archives
27 papers · ranked by Valyu relevance
Junhao Wei, Wenxuan Zhu, Qingyang Xu, Yanxiao Li + 10 more
Metaheuristic algorithms have been widely applied to complex optimization problems due to their independence from gradient information, strong global search capability, and robust performance. The Sparrow Search Algorithm (SSA), characterized by its simple structure and ease of implementation, nevertheless suffers from…
Hui Yu, Mengyuan Xie, Zhanxi Zhou, Heming Jia + 1 more
The Dung Beetle Optimizer (DBO) has shown promise in solving complex optimization problems, yet it often suffers from premature convergence and limited accuracy. To overcome these limitations, this paper proposes the Enhanced Reproductive Dung Beetle Optimizer (ERDBO). The ERDBO introduces a three-stage mechanism: (1)…
Kumar, Varun, Karniadakis, George Em
The engineering design process frequently demands expertise from diverse domains, often leading to complex collaborations and iterative refinement cycles. Traditional approaches can be resource-intensive and prone to inefficiencies. To address this, we formalize the engineering design process through a novel…
Zhongzhen Yan, Yi Yu, Yuan Cao, Jie Gao + 1 more
To address the challenges of high-dimensional nonlinearity, multimodal landscapes, and stringent constraints prevalent in modern engineering design, traditional meta-heuristic algorithms often suffer from a loss of population diversity and premature convergence. Inspired by the social collaborative predation and…
Zhenkun Lu, Mingbin Tang, Meng Li, Xiangyun Meng + 4 more
The Horned Lizard Optimization Algorithm (HLOA) is a newly proposed swarm intelligence optimizer mimicking the defensive and survival behaviors of horned lizards. The original HLOA suffers evident drawbacks when tackling high-dimensional, multimodal and heavily constrained complicated optimization problems. Rapid…
Sunde Wang, Yejun Zheng, Pu Wang, Zihao Cheng + 1 more
To address the defects of the traditional egret swarm optimization algorithm (ESOA) in high-dimensional complex optimization problems, such as low optimization accuracy, weak ability to escape from local extrema, rapid decay of population diversity, and insufficient efficiency in the late convergence stage, an improved…
M. Moustapha, B. Sudret
Reliability-based design optimization (RBDO) is traditionally formulated as a nested optimization and reliability problem. Although surrogate models are generally employed to improve efficiency, the approach remains computationally prohibitive in high-dimensional settings. This paper proposes a novel RBDO framework…
Sunde Wang, Beilei Yin, Pu Wang, Zihao Cheng + 1 more
To address the inherent limitations of the traditional Animated Oat Optimization Algorithm (AOO), including poor uniformity of initial random population distribution and insufficient dynamic balance between global exploration and local exploitation, this paper proposes an Enhanced Animated Oat Optimization Algorithm…
Tuğrul Yağbasan, Ömür Akyazı, Hayati Türe, Bekir Dizdaroğlu + 2 more
Biomimetic optimizers are increasingly used to solve complex engineering problems, yet their performance depends strongly on how effectively they preserve diversity while maintaining selection pressure toward promising regions. In this study, the Starfish Optimization Algorithm (SFOA) is enhanced through…
Authors not listed
This article presents an overview about the state of the art in the development of structured packings for distillation applications. The focus is on highlighting different approaches including heuristic development cycles, the development of new packing structures, 3D-printing as tool for manufacturing, and…
Luuk Oerlemans, Steven Westerhof, Theo Hofman
This article addresses the combinatorial complexity inherent in modern high-tech system design by presenting automation-in-design (AiD) as a transformative paradigm. We propose computational design synthesis (CDS), a framework utilising deep learning and generative AI to automate the creation of novel systems. Two case…
Varun Kumar, George Em Karniadakis
This paper introduces a multi-agent framework guided by Large Language Models (LLMs) to assist in the early stages of engineering design, a phase often characterized by vast parameter spaces and inherent uncertainty. Operating under a human-in-the-loop paradigm and demonstrated on the canonical problem of aerodynamic…
Ke Wang, Yifan Tang, Hoang Nguyen Vu, Faez Ahmed + 1 more
Deep generative models for engineering design often require substantial computational cost, large training datasets, and extensive retraining when design requirements or datasets change, limiting their applicability in real-world engineering design workflow. In this work, we propose a zero-shot generation framework for…
Authors not listed
Designing efficient photoreactors remains challenging due to the complex interplay of light transport phenomena, shaped by reflection, scattering and absorption processes. Here, we introduce a workflow that integrates ray-tracing digital twins with multi-objective Bayesian optimization to autonomously design…
Haoyu Wang, Mengyao Zhang, Chengyu Zhang, Siwei He + 4 more
The rational design of microbial cell factories for high bioproduction remains a key challenge in metabolic engineering. While advanced modelling frameworks incorporating protein resource allocation, such as enzyme-constrained models (ecGEMs) and Expression and Thermodynamic Flux (ETFL), provide superior predictive…
Paul van Lent, Sara Moreno Paz, Joep Schmitz, Thomas Abeel
Design-Build-Test-Learn (DBTL) cycles are a widely employed engineering framework in metabolic engineering. Nonetheless, their performance depends on a wide range of experimental and algorithmic design choices, whose combined effects on the successful optimization of microbial strains remain an open question. In this…
Authors not listed
Background: Batch reactor process optimization has traditionally relied on Analysis of Variance (ANOVA) for factor effect quantification. However, Structural Equation Modeling (SEM) and machine learning (ML) offer complementary mechanistic and predictive capabilities that remain underexplored in chemical engineering…
Odin Zhang, Jiaqi Wang, Tuscan Rock Thompson, Ziyi You + 3 more
Biomolecular interactions, including protein–protein interactions, protein–nucleic acid recognition, and protein–small molecule binding, underlie a wide range of biological processes and therapeutic mechanisms. Although recent de novo design methods can generate candidate binders for diverse molecular targets…
Authors not listed
Continuous manufacturing processes offer significant advantages over batch processes, including easier scalability, reduced costs, lower raw material and solvent consumption, and improved energy efficiency. A robust techno-economic assessment is therefore essential to evaluate and facilitate the adoption of such…
Dulitha P. Kulathunga, Divyanshu Shukla, Davit Potoyan
Protein engineering is limited less by generating variants than by the cost of evaluating them, so designing under a tight budget demands sequence features that let a model learn fitness from very few examples. We introduce ALSEBO (Active Learning Sequence Exploration via Bayesian Optimization), which couples a…
Elias Theodorou, Michael Stadler, Claes Gustafsson, Mark Welch
Mammalian cell lines are the preferred hosts for producing commercially relevant therapeutic proteins such as antibodies, multispecifics, and cytokine fusion proteins. Even though significant investment is made to optimize upstream and downstream processes, the optimal gene design parameters for heterologous…
Authors not listed
Bayesian optimization (BO) has become increasingly important for experimental optimization across scientific domains, yet implementing BO pipelines requires significant programming expertise and familiarity with specialized frameworks. This creates a barrier for domain experts who could benefit from BO but lack the…
Authors not listed
This conceptual paper introduces the Adaptive Multi-Resolution Modeling Framework (AMRMF), a novel technique designed to revolutionize chemical engineering by integrating multi-scale simulations, quantum-inspired algorithms, advanced uncertainty quantification, and Bayesian inference. The framework bridges theoretical…
Authors not listed
Artificial intelligence (AI) is reshaping chemical engineering. Still, its role in safety-critical operations is limited because we rarely see tools that link physical models with data-driven methods. This study brings together three elements: physics-constrained neural networks, uncertainty quantification, and a…
Changin Oh, Kathleen P. Wilkie
We present the Toroidal Search Algorithm (TSA), a novel population-based metaheuristic optimization method inspired by the topology of a torus. Conventional metaheuristics frequently suffer from boundary stagnation, a phenomenon that severely degrades performance in bounded and high-dimensional search spaces. TSA…
Xiaopeng Xu, Chenjie Feng, Chao Zha, Wenjia He + 3 more
Computational protein design is often constrained by slow, complex, inaccessible, and highly sophiscated and expert-dependent workflows that hinder its transferrability and generalization power for broader applications. We present ProteinMCP, an agentic AI framework designed to accelerate and democratize protein…
Authors not listed
This paper introduces Optical Fiber Chemistry (OFC) as a fourth-generation catalytic paradigm, distinguished not by incremental improvements in catalyst materials but by a fundamental reconfiguration of the catalytic reaction platform. By employing optical fibers as active photonic control elements, OFC achieves gen-…