Tootfinder

A Scoping Review of Physics Informed Machine Learning for Wave Propagation Modeling in Seismology
\'Oscar Rinc\'on-Carde\~no, Gregorio P\'erez-Bernal, Silvana Montoya-Noguera, Nicol\'as Guar\'in-Zapata
https://arxiv.org/abs/2607.00178 https://arxiv.org/pdf/2607.00178 https://arxiv.org/html/2607.00178
arXiv:2607.00178v1 Announce Type: new
Abstract: \emph{Background:} Standard numerical methods accurately simulate seismic waves but are computationally expensive, particularly for inverse problems. Machine learning approaches have been proposed as alternatives that can reduce computational cost while maintaining acceptable physical accuracy. \emph{Objective:} To map how physics-informed machine learning methods have been applied to seismic wave propagation modeling based on partial differential equations. \emph{Methods:} A scoping review was conducted using the OpenAlex and Scopus databases. Selected studies were classified by problem type (forward or inverse) and machine learning strategy to identify research trends, methodological patterns, and gaps in the literature. \emph{Results:} Physics-informed machine learning has been applied to both forward modeling and inversion in seismology, often reaching accuracy comparable to standard numerical methods at lower computational cost. Application of three mechanisms for incorporating physical knowledge were identified: observational bias, inductive bias, and learning bias. To evaluate methodological reproducibility of a representative method, the original PINN framework was replicated in PyTorch, obtaining results consistent with and in most cases more accurate than those originally reported. From the reviewed literature, limitations remain in benchmarking consistency, training cost, and scalability to three-dimensional and experimentally validated problems. \emph{Conclusions:} Standard numerical methods remain the basis of seismological workflows, while physics-informed machine learning offers complementary approaches that are useful for inverse problems and surrogate modeling. Future work should focus on consistent benchmarking, hybrid formulations, and validation under realistic geophysical conditions.
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"Hawaii is turning ocean plastic and fishing nets into roads"
#Hawaii #Oceans #Environemnt #Plastic

Hawaii is turning ocean plastic and fishing nets into roads
Hawaii researchers are giving old fishing nets and recycled plastic a second life by mixing them into asphalt roads. Early tests found these roads didn't release more plastic particles than standard pavement, with tire wear overwhelming any plastic signal from the recycled material. If future studies confirm the roads are durable, the technology could help tackle both marine pollution and overflowing landfills.

Physical Properties of 6.7 Million Galaxies from the DESI Bright Galaxy Survey: Spectral Fitting and Systematic Tests with Mock Spectra
Niu Li, Hu Zou, Jinfu Gou, Weijian Guo, Wenxiong L, Haoming Song, Jipeng Sui, Xi Tan, Yunao Xiao, Jingyi Zhang
https://arxiv.org/abs/2607.19162 https://arxiv.org/pdf/2607.19162 https://arxiv.org/html/2607.19162
arXiv:2607.19162v1 Announce Type: new
Abstract: We present a comprehensive analysis of the physical properties of galaxies in the Dark Energy Spectroscopic Instrument (DESI) Data Release 1 (DR1) Bright Galaxy Survey (BGS), based on full spectral fitting of $\sim 6.7$ million galaxy spectra. Using a customized spectral fitting pipeline, we derive key physical parameters including stellar mass, stellar velocity dispersion, stellar population age, dust attenuation, and emission-line properties. To quantify the reliability and systematic uncertainties of our measurements, we construct a large set of mock spectra that closely reproduce the observed properties of DESI data, including realistic noise and spectral features. By comparing the recovered parameters with the known inputs, we assess the performance of the spectral fitting as a function of stellar continuum signal-to-noise ratio (S/N, defined as the ratio of the median continuum flux to its associated error) and redshift. We find that stellar masses can be robustly recovered with negligible bias for spectra with $\mathrm{S/N} \gtrsim 5$, while low-S/N spectra ($\mathrm{S/N} \lesssim 5$) show a mild systematic overestimation of $\sim 0.1$ dex and increased scatter. Similar trends are observed for stellar population parameters, while emission-line fluxes are recovered with high accuracy and minimal bias. We further validate our stellar mass estimates by comparison with independent measurements from photometric spectral energy distribution fitting, finding good overall consistency within the expected systematic uncertainties. The value-added catalog presented in this work enables a wide range of statistical studies of galaxy evolution with DESI, and provides a foundation for future analyses.
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#Science #Cancer #Cure #Frogs
When people decry the cost of saving a species or environmental studies before construction...or hell mitigating climate change before it kills off so many species.
the Japanese Tree Frog has a bacteria that literally kills cancer.
And beyond that, it teaches the body to attack any future re-occurrence of that cancer.
https://www.youtube.com/shorts/VGvuEc_tLQY