Exascale incident on November 6, 2026: The intermittent fault affecting the Vodafone DSL interconnect in Manchester has returned following the earlier period of stability. Custome Started 9:28 PM UTC, resolved ongoing (● 6m). Affected: Vodafone WDSL.

Exascale incident on November 6, 2026: We are investigating an intermittent fault affecting the Vodafone DSL interconnect in Manchester. We are seeing elevated errors on the port Started 2:04 PM UTC, resolved ongoing (● 6m). Affected: Vodafone WDSL.

Pour la première fois depuis 2017, une machine chinoise domine le classement mondial des supercalculateurs. Publiée le 23 juin 2026 à Hambourg, la 67e édition du TOP500 compte désormais cinq systèm...

NetApp wants to acquire Peak:AIO to provide the kind of orchestration needed to build high-performance exascale data infrastructures targeting AI workloads.

Newswise — Groundwater table depth (GWTD) influences how water moves through soils, rivers, and ecosystems. Hence, it also affects how water and energy move between the land and atmosphere. However, Earth system models often struggle to accurately represent groundwater at global scales. This study investigated whether using machine learning to calibrate models could improve the Energy Exascale Earth System Model (E3SM) Land Model’s simulation of GWTD. Researchers developed a framework supported by machine learning. They started with a stand-in computer model that they had trained on land model simulations. They then combined the surrogate model with global observations of GWTD to adjust key parameters related to water runoff in E3SM. This approach improved how well E3SM modeled GWTD compared to simulations that used default parameters. In particular, it improved E3SM’s predictions within the 1 to 5 meter depth range. At this range, groundwater can influence surface processes. However, this representation of GWTD did not consistently improve how E3SM represented other variables related to water. These variables included soil moisture, runoff, and evapotranspiration. The Impact This study provided a test of how using machine learning to calibrate an Earth system model can improve how it simulates groundwater. It demonstrated that machine...

Mechanism-Informed Multimodal Generative AI for Causal and Dynamical Modelling in Biomedical Research

Groundwater table depth (GWTD) influences how water moves through soils, rivers, and ecosystems. Hence, it also affects how water and energy move between the land and atmosphere. However, Earth system models often struggle to accurately represent groundwater at global scales. This study investigated whether using machine learning to calibrate models could improve the Energy Exascale Earth System Model (E3SM) Land Model's simulation of GWTD. Researchers developed a framework supported by machine learning. They started with a stand-in computer model that they had trained on land model simulations. They then combined the surrogate model with global observations of GWTD to adjust key parameters related to water runoff in E3SM. This approach improved how well E3SM modeled GWTD compared to simulations that used default parameters. In particular, it improved E3SM's predictions within the 1 to 5 meter depth range. At this range, groundwater can influence surface processes. However, this representation of GWTD did not consistently improve how E3SM represented other variables related to water. These variables included soil moisture, runoff, and evapotranspiration. The Impact This study provided a test of how using machine learning to calibrate an Earth system model can improve how it simulates groundwater. It demonstrated that machine learning can...

Newswise — Groundwater table depth (GWTD) influences how water moves through soils, rivers, and ecosystems. Hence, it also affects how water and energy move between the land and atmosphere. However, Earth system models often struggle to accurately represent groundwater at global scales. This study investigated whether using machine learning to calibrate models could improve the Energy Exascale Earth System Model (E3SM) Land Model’s simulation of GWTD. Researchers developed a framework supported by machine learning. They started with a stand-in computer model that they had trained on land model simulations. They then combined the surrogate model with global observations of GWTD to adjust key parameters related to water runoff in E3SM. This approach improved how well E3SM modeled GWTD compared to simulations that used default parameters. In particular, it improved E3SM’s predictions within the 1 to 5 meter depth range. At this range, groundwater can influence surface processes. However, this representation of GWTD did not consistently improve how E3SM represented other variables related to water. These variables included soil moisture, runoff, and evapotranspiration. The Impact This study provided a test of how using machine learning to calibrate an Earth system model can improve how it simulates groundwater. It demonstrated that machine...

Schedule 13G filed Oct 29. Who filed, the stake, and how it changed.

Reported Friday, Oct 30 - 4:24pm EDT (TheFly subscription required to read more.)