Quantitative assessment of PINN inference on experimental data for gravity currents flows by Mickaël Delcey, Yoann Cheny, Jean Schneider, Simon Becker, Sébastien Kiesgen De Richter

“…PINNs are able to solve ill-posed inverse problems training on sparse and noisy data, and therefore can be applied to real engineering applications. …”
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Two-Stage GPR Image Inversion Method Based on Multi-Scale Dilated Convolution and Hybrid Attention Gate by Mingze Wu, Qinghua Liu, Shan Ouyang

“…In practical applications, the complexity and nonuniformity of underground structures bring noise and clutter interference, making GPR inversion problems more challenging. To address these issues, this study proposes a two-stage GPR image inversion network called MHInvNet based on multi-scale dilated convolution (MSDC) and hybrid attention gate (HAG). …”
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Proximal Neural Networks based reconstruction for few-view CT applications by Hoang Trieu Vy Le, Caroline Bossuyt, Julie Escoda, Marius Costin, Jan De Beenhouwer, Jan Sijbers

“…Recent advancements in Plug-and-Play (PnP) algorithms have shown promise for solving imaging inverse problems by utilizing the capabilities of Gaussian denoising algorithms to handle complex optimization tasks. …”
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Research Progress on Failure Probability Analysis of Earth-Rockfill Dams Based on Random Field Models by WANG Wei, LIAO Zhihao, LIAO Jielin

“…This paper summarized domestic and international application examples of random field models in failure probability analysis of earth-rockfill dams from four aspects: seepage, stability, static, and dynamic analysis, including obtaining more accurate seepage, stability, and static dynamic calculation results, analyzing the sensitivity of material parameters of earth-rockfill dams, and playing an important role in parameter inversion problems and establishing dam monitoring models. …”
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Application of physics-informed neural networks (PINNs) solution to coupled thermal and hydraulic processes in silty sands by Yuan Feng, Jongwan Eun, Seunghee Kim, Yong-Rak Kim

“…Recently, physics-informed neural networks (PINNs), which incorporate partial differential equations (PDEs) to solve forward and inverse problems, have attracted increasing attention in machine learning research. …”
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A Discrete Dipole Approximation Solver Based on the COCG-FFT Algorithm and Its Application to Microwave Breast Imaging by Samar Hosseinzadegan, Andreas Fhager, Mikael Persson, Paul Meaney

“…We introduce the discrete dipole approximation (DDA) for efficiently calculating the two-dimensional electric field distribution for our microwave tomographic breast imaging system. For iterative inverse problems such as microwave tomography, the forward field computation is the time limiting step. …”
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A Novel Improved Maximum Entropy Regularization Technique and Application to Identification of Dynamic Loads on the Coal Rock by Chunsheng Liu, Chunping Ren

“…Selecting optimal calculated parameters pi is helpful to overcome the ill-condition of dynamic load signals identification and to obtain the stable and approximate solutions of inverse problems in practical engineering. Meanwhile, the proposed IMER technique can also play a guiding role for the coal-rock interface identification.…”
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From lab to landscape-scale experiments for the morphodynamics of sand dunes by Claudin, Philippe, Courrech du Pont, Sylvain, Narteau, Clément

“…This understanding can serve as a foundation for further investigations, including the interpretation of dune landscapes and the resolution of inverse problems.…”
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Segmented algorithm for three-dimensional reconstruction in linear scan geometry by Т. Г. Віхтинська, К. Є. Лапітан, К. Е. Нємченко

“…Materials and Methods: The work utilizes methods of integral transforms and computer modeling to solve inverse problems arising in computer tomography. Results: An analytical inversion formula was obtained for three-dimensional computer tomography with linear scan geometry and segmentation. …”
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On Damage Identification in Planar Frames of Arbitrary Size by I. Fiore, A. Greco, A. Pluchino

“…The natural frequencies obtained by means of the proposed approach are used for the solution of two different inverse problems, which concern the identification of, respectively, the mechanical characteristics of the constitutive material and the location and intensity of the damage. …”
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Stratifications and foliations in phase portraits of gene network models by V. P. Golubyatnikov, A. A. Akinshin, N. B. Ayupova, L. S. Minushkina

“…In the preparation of numerical experiments with such mathematical models, it is useful to start with studies of qualitative behavior of ensembles of trajectories of the corresponding dynamical systems, in particular, to estimate the highest likelihood domain of the initial data, to solve inverse problems of parameter identification, to list the equilibrium points and their characteristics, to localize cycles in the phase portraits, to construct stratification of the phase portraits to subdomains with different qualities of trajectory behavior, etc. …”
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Effective Partitioning Method With Predictable Hardness for CircuitSAT by Konstantin Chukharev, Irina Gribanova, Dmitry Ivanov, Stepan Kochemazov, Victor Kondratiev, Alexander Semenov

“…We demonstrate the effectiveness of the proposed constructions by applying them to some problems associated with CircuitSAT, in particular, Logical Equivalence Checking benchmarks, Automated Test Pattern Generation benchmarks and the inversion problems of some cryptographic functions.…”
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Advances in the pilot point inverse method: Où En Sommes-Nous maintenant? by White, Jeremy, Lavenue, Marsh

“…Herein, we provide an update to de Marsily’s paper entitled “Four Decades of Inverse Problems in Hydrogeology” [De Marsily et al., 2000], but with a particular focus on the incredible adoption and advancement of de Marsily’s PPM and related inverse techniques over the last twenty years in the field of predictive groundwater modeling.Much has been written about the vast array of inverse techniques developed by researchers and practitioners since the 1960s. de Marsily’s PPM, like many methods developed in the late 70s and early 80s, structured its approach to parameterization to overcome many of the challenges of applying inverse methods to real world problems, namely, limited head and transmissivity data relative to the number of unknowns to be estimated, measurement errors, inferred covariance structures of the state variables, and limited computational resources. …”
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