Dynamic Inversion Method for Concrete Gravity Dam on Soft Rock Foundation by Guanglin Yin, Chaoning Lin, Taozhen Sheng, Wenbo Xue, Tongchun Li, Siyu Chen

“…To address this limitation, an improved particle swarm optimization (PSO) algorithm is developed for dynamic parameter inversion, combining real-time monitoring data with finite element modeling to evaluate the time-varying elastic modulus of the foundation. …”
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Inversion analysis of constitutive relations of blade spar laminates with wrinkle defects by Ning Sun, Bo Zhou, Haocheng Zheng, Yingwei Wang

“…Four sets of uniaxial tension tests were conducted on specimens with varying defect sizes to obtain their corresponding load-displacement curves. A hybrid optimization method integrating Bayesian regularized backpropagation neural networks and particle swarm optimization was developed to inversely determine the constitutive parameters of composite laminates. …”
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Sensitivity Analysis of a CPAM Inverse Algorithm for Composite Laminates Characterization by Farshid Masoumi, Ahmad Ghasemi Ghalebahman

“…The numerical solutions are based on a commercial finite element package for efficiently identifying the material properties. An inverse method based on particle swarm optimization algorithm is further provided using MATLAB software. …”
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Parallel Bayesian Optimization of Thermophysical Properties of Low Thermal Conductivity Materials Using the Transient Plane Source Method in the Body-Fitted Coordinate by Huijuan Su, Jianye Kang, Yan Li, Mingxin Lyu, Yanhua Lai, Zhen Dong

“…A parallel Bayesian optimization algorithm based on a multi-objective hybrid strategy (MHS) is proposed based on an inverse problem. …”
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Wavefield-reconstruction-based full waveform inversion on noisy data in seismic exploration by Yuwei Yu, Yuwei Yu, Yuwei Yu, Zhefeng Wei, Zhefeng Wei, Zhefeng Wei, Xiaofeng Jia, Xiaofeng Jia, Xiaofeng Jia, Chenghong Zhu, Chenghong Zhu, Chenghong Zhu

“…Full waveform inversion (FWI) is commonly used in seismic exploration to calculate parameters of the medium, such as velocity, from the signal as it passes through the medium. …”
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Tackling multimodal device distributions in inverse photonic design using invertible neural networks by Michel Frising, Jorge Bravo-Abad, Ferry Prins

“…We show how conditional generative neural networks can be used to efficiently find nanophotonic devices with desired properties, also known as inverse photonic design. Machine learning has emerged as a promising approach to overcome limitations imposed by the dimensionality and topology of the parameter space. …”
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Reliable and efficient magnetic data inversion for resource detection using a hybrid bat algorithm by Khalid S. Essa, Mahmoud Elhussein, Omar A. Gomaa, Zein E. Diab

“…This approach prioritize geometric parameters to define the spatial configuration of magnetic sources, assuming constant petrophysical properties (e.g., magnetization intensity, susceptibility contrast) to simplify the inversion process. …”
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Optimization of PES-based Hollow fiber membranes incorporating MgO-modified activated carbon via response surface methodology for enhanced pure water permeability by Farkhonde Arabloo, Sirus Javadpour

“…High-resolution transmission electron microscopy (HR-TEM), scanning electron microscopy (SEM), and X-ray diffraction (XRD) were utilized to examine the morphological and chemical properties of the AC-MgO particles. To optimize the fabrication process, the effects of four key parameters: AC-MgO particle concentration (0.09–0.36 wt%), bore fluid flow rate (3.5–9.5 ml/min), dope solution flow rate (1.5–3.5 ml/min), and air gap distance (2–42 cm), were analyzed using response surface methodology (RSM) with a central composite design. …”
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DWE: Research on Water Quality Inversion and Scalability in Small Watersheds Using UAV Remote Sensing Data by Hao Li, Zhijun Xie, Xing Jin, Changchun Peng, Cheng Ren, Yunfei Mao, Kefan Zhou, Yingying Li

“…However, traditional water quality inversion models ignore the complex optical properties of water bodies, resulting in the performance of models that are often limited and cannot be reused across time and space. …”
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Reconstruction of Induced Polarization Information from Electromagnetic Data Using Neural Network Approach by Mohamed Elkattan

“…In this paper, an inversion setting is presented to estimate the induced polarization parameters of a stratified, polarizable, and layered medium from scattered electromagnetic fields. …”
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Inverse Design of Broadband Artificial Magnetic Conductor Metasurface for Radar Cross Section Reduction Using Simulated Annealing by Haoda Xia, Xiaoyu Liang, Bowen Jia, Pei Shi, Zhihong Chen, Shi Pu, Ning Xu

“…Following optimization, the properties of the metasurface were experimentally verified. …”
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Customizable metamaterial design for desired strain-dependent Poisson’s ratio using constrained generative inverse design network by Sukheon Kang, Hyunggwi Song, Hyun Seok Kang, Byeong-Soo Bae, Seunghwa Ryu

“…A deep neural network (DNN) is then trained to map input geometric parameters to these principal components, with the architecture optimized using grid search. …”
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Deep-learning enabled generalized inverse design of multi-port radio-frequency and sub-terahertz passives and integrated circuits by Emir Ali Karahan, Zheng Liu, Aggraj Gupta, Zijian Shao, Jonathan Zhou, Uday Khankhoje, Kaushik Sengupta

“…These structures (radiative and non-radiative, single-port and multi-ports) are subsequently optimized through ad-hoc methods and parameter sweeps. …”
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Enhancing synchrotron radiation micro-CT images using deep learning: an application of Noise2Inverse on bone imaging by Yoshihiro Obata, Dilworth Y. Parkinson, Daniël M. Pelt, Claire Acevedo

“…Noise present during data acquisition from the testing setup was determined to be the primary source of concern for Noise2Inverse viability. While these limitations exist, incorporating dose calculations and optimal imaging parameters enables self-supervised deep learning methods such as Noise2Inverse to be integrated into existing experiments to decrease radiation dose.…”
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Inverse design of high-strength medium-Mn steel using a machine learning-aided genetic algorithm approach by Jin-Young Lee, Seung-Hyun Kim, Hyun-Bin Jeong, KeunWon Lee, KiSub Cho, Young-Kook Lee

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A High-Precision Real-Time Temperature Acquisition Method Based on Magnetic Nanoparticles by Yuchang Zhu, Li Ke, Yijing Wei, Xiao Zheng

“…A single algorithm is designed for temperature inversion, and a joint algorithm is proposed to optimize the performance of the single algorithm. …”
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Mathematical Optimization in Machine Learning for Computational Chemistry by Ana Zekić

“…Machine learning (ML) is transforming computational chemistry by accelerating molecular simulations, property prediction, and inverse design. Central to this transformation is mathematical optimization, which underpins nearly every stage of model development, from training neural networks and tuning hyperparameters to navigating chemical space for molecular discovery. …”
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Analysis of Requirements and the Feasible Limit for Error Reduction in Two-Parameter Magnetic Determination of Steels’ Hardness by S. G. Sandomirski

“…All measurements of mechanical properties of materials in the magnetic structural analysis are indirect and relationships between the measured parameters are correlated. …”
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Statistical Identification of Parameters for Damaged FGM Structures with Material Uncertainties in Thermal Environment by Yalan Xu, Yu Qian, Kongming Guo

“…Considering that the statistic numerical characteristics are often required in the probability-based damage identification and safety assessment of functionally graded material (FGM) structures, an stochastic model updating-based inverse computational method to identify the second-order statistics (means and variances) of material properties as well as distribution of constituents for damaged FGM structures with material uncertainties is presented by using measurable modal parameters of structures. …”
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Compressive properties analysis of mono-sized fragmented coal and rock by Dingyi Hao, ShiKun Xu, Shihao Tu, Hongbin Zhao, Long Tang

“…Abstract The compressive properties of fragmented coal and rock aggregates within goaf critically influences surface subsidence dynamics and reservoir development strategies. …”
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