Inverse modeling is a class of computational and statistical methods that infer unknown model parameters, inputs, or system states from observed outputs, given a forward model that maps parameters to observables. It typically involves solving ill-posed or underdetermined inverse problems, requiring regularization, prior information, or Bayesian formulations to obtain stable and identifiable solutions. Techniques include deterministic optimization (e.g., least squares, variational methods), stochastic sampling (e.g., Markov chain Monte Carlo), and adjoint-based sensitivity analysis to efficiently compute gradients. Inverse modeling is central to parameter estimation, data assimilation, and model calibration across disciplines such as geophysics, atmospheric science, and systems biology.
Near-infrared spectroscopy, or fNIRS, offers a way to monitor brain activity without surgery or radiation by tracking changes in blood flow and oxygenation. Light sources placed on the scalp send near-infrared light into ...
Optics & Photonics
Apr 7, 2026
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13
Oak gall wasps and their predators don't have the panache of butterflies, but they're attracting growing interest among both scientists and naturalists.
Plants & Animals
Aug 19, 2025
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237
Kirigami is a traditional Japanese art form that entails cutting and folding paper to produce complex three-dimensional (3D) structures or objects. Over the past decades, this creative practice has also been applied in the ...
A research team led by Prof. Hu Bingliang and Prof. Yu Tao from the Xi'an Institute of Optics and Precision Mechanics (XIOPM) of the Chinese Academy of Sciences has developed the physicochemical-informed spectral Transformer ...
Environment
Apr 9, 2025
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36
Research that eliminates the guesswork in developing advanced 3D printed materials could help accelerate the development of new forms of "self-sensing" airplanes, robots, bridges and more.
Nanophysics
Oct 7, 2024
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71
Computer-generated holography (CGH) provides an approach to digitally modulate a given wavefront. This technology, partly inherited from optical holography and partly advanced by the progress of computing technology, has ...
Optics & Photonics
Aug 5, 2024
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76
Data assimilation is a mathematical discipline that integrates observed data and numerical models to improve the interpretation and prediction of dynamical systems. It is a crucial component of Earth sciences, particularly ...
General Physics
Jun 13, 2024
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