Unraveling hidden rules behind the wet-to-dry transition of bubble array by glass-box physics rule learner

Predicting individual large earthquakes (EQs)’ locations, magnitudes, and timing remains unreachable. The author’s prior study shows that individual large EQs have unique signatures obtained from multi-layered data transformations. Via spatio-temporal convolutions, decades-long EQ catalog data are transformed into pseudo-physics quantities (e.g., energy, power, vorticity, and Laplacian), which turn into surface-like information via Gauss curvatures. Using these new features, a rule-learning machine learning approach unravels promising prediction rules. This paper suggests further data transformation via Fourier transformation (FT). Results show that FT-based new feature can help sharpen the prediction rules. Feasibility tests of large EQs ($$M\ge$$ M ≥ 6.5) over the past 40 years in the western U.S. show promise, shedding light on data-driven prediction of individual large EQs. The handshake among ML methods, Fourier, and Gauss may help answer the long-standing enigma of seismogenesis.

Section: Resultsmentioning

confidence: 96%

Sharpen data-driven prediction rules of individual large earthquakes with aid of Fourier and Gauss

Cho

2023

“…Finding transparent link functions (LFs) is done by the Bayesian evolution algorithm on the ground of the so-called “glass-box” rule-learning, which has been successfully applied to other complex physics phenomena suffering from the absence of first principles across wide length scales—the nanoscale tribo-charging 32 , the millimeter-scale wet-to-dry bubble transition 33 , the centimeter-scale composite heterogeneous materials 34 , 35 , and the extreme failures in earth lithosphere 36 . Detailed descriptions of the LFs and the glass-box rule-learning are presented in Method and Supplementary Information.…”

Section: Resultsmentioning

confidence: 99%

Pursuit of hidden rules behind the irregularity of nano capillary lithography by hybrid intelligence

Cho,

Ji,

Kim

2023

Self Cite

Nature finds a way to leverage nanotextures to achieve desired functions. Recent advances in nanotechnologies endow fascinating multi-functionalities to nanotextures by modulating the nanopixel’s height. But nanoscale height control is a daunting task involving chemical and/or physical processes. As a facile, cost-effective, and potentially scalable remedy, the nanoscale capillary force lithography (CFL) receives notable attention. The key enabler is optical pre-modification of photopolymer’s characteristics via ultraviolet (UV) exposure. Still, the underlying physics of the nanoscale CFL is not well understood, and unexplained phenomena such as the “forbidden gap” in the nano capillary rise (unreachable height) abound. Due to the lack of large data, small length scales, and the absence of first principles, direct adoptions of machine learning or analytical approaches have been difficult. This paper proposes a hybrid intelligence approach in which both artificial and human intelligence coherently work together to unravel the hidden rules with small data. Our results show promising performance in identifying transparent, physics-retained rules of air diffusivity, dynamic viscosity, and surface tension, which collectively appear to explain the forbidden gap in the nanoscale CFL. This paper promotes synergistic collaborations of humans and AI for advancing nanotechnology and beyond.

“…S2 C, the over-smoothing effect is notable. In heterogeneous materials or composite structures, this spatial convolved II may help ML understand internal complexity as scientists do 32 – 34 , 46 ,] .…”

Section: Methodsmentioning

confidence: 99%

Gauss curvature-based unique signatures of individual large earthquakes and its implications for customized data-driven prediction

Cho

2022

Self Cite

Statistical descriptions of earthquakes offer important probabilistic information, and newly emerging technologies of high-precision observations and machine learning collectively advance our knowledge regarding complex earthquake behaviors. Still, there remains a formidable knowledge gap for predicting individual large earthquakes’ locations and magnitudes. Here, this study shows that the individual large earthquakes may have unique signatures that can be represented by new high-dimensional features—Gauss curvature-based coordinates. Particularly, the observed earthquake catalog data are transformed into a number of pseudo physics quantities (i.e., energy, power, vorticity, and Laplacian) which turn into smooth surface-like information via spatio-temporal convolution, giving rise to the new high-dimensional coordinates. Validations with 40-year earthquakes in the West U.S. region show that the new coordinates appear to hold uniqueness for individual large earthquakes ($$M_w \ge 7.0$$ M w ≥ 7.0 ), and the pseudo physics quantities help identify a customized data-driven prediction model. A Bayesian evolutionary algorithm in conjunction with flexible bases can identify a data-driven model, demonstrating its promising reproduction of individual large earthquake’s location and magnitude. Results imply that an individual large earthquake can be distinguished and remembered while its best-so-far model can be customized by machine learning. This study paves a new way to data-driven automated evolution of individual earthquake prediction.