Constrained reduced-order modeling using bounded Gaussian processes for physically consistent reacting flow predictions by Muhammad Azam Hafeez, Alberto Procacci, Axel Coussement, Alessandro Parente

“…These bounded likelihood functions explicitly model the observational noise in the bounded space and use variational inference to approximate analytically intractable posterior distributions, producing GP estimations that satisfy physical constraints by construction. …”
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Advancing Cosmological Parameter Estimation and Hubble Parameter Reconstruction with Long Short-term Memory and Efficient Kolmogorov–Arnold Networks by Jiaxing Cui, Marek Biesiada, Ao Liu, Cuihong Wen, Tonghua Liu, Jieci Wang

“…LSTM networks are employed to extract features from observational data, enabling accurate parameter inference and posterior distribution estimation without relying on solvable likelihood functions. This method achieves performance comparable to traditional Markov Chain Monte Carlo techniques, offering a computationally efficient alternative for high-dimensional parameter spaces. …”
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A statistical framework for modelling migration corridors by Tristan A. Nuñez, Mark A. Hurley, Tabitha A. Graves, Anna C. Ortega, Hall Sawyer, Julien Fattebert, Jerod A. Merkle, Matthew J. Kauffman

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Improving the Minimum Free Energy Principle to the Maximum Information Efficiency Principle by Chenguang Lu

“…However, it has a theoretical flaw, a possibility of being misunderstood, and a limitation (only likelihood functions are used as constraints). This paper first introduces the semantic information G theory and the <i>R</i>(<i>G</i>) function (where <i>R</i> is the minimum mutual information for the given semantic mutual information <i>G</i>). …”
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A Correction Method for Hardening Artifacts in CT Images Based on Integral Invariance by Yuxin LIU, Jiaotong WEI, Xiaojie ZHAO, Ping CHEN, Jinxiao PAN

“…The decomposition model for the X-ray transmission images is constructed using the maximum likelihood function of a Gaussian distribution as the objective function under the constraint of the projection integral invariance at different angles. …”
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Leveraging large language models to predict antibody biological activity against influenza A hemagglutinin by Ella Barkan, Ibrahim Siddiqui, Kevin J. Cheng, Alex Golts, Yoel Shoshan, Jeffrey K. Weber, Yailin Campos Mota, Michal Ozery-Flato, Giuseppe A. Sautto

“…Models that predict antibody biological activity enable in silico evaluation of binding and functional properties; such models can prioritize antibodies with the highest likelihood of success in laboratory testing procedures. …”
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