Technology Engineering OceanGate's'Titan' went on 7 dives with a damaged hull before implosion Investigators found that the submersible's exterior featured'multiple anomalies' as early as 2022. Breakthroughs, discoveries, and DIY tips sent every weekday. The United States National Transportation Safety Board (NTSB) recently concluded its investigation into the OceanGate submersible disaster . According to the summary report released on October 15, an already weakened hull caused the deep sea tourist vessel to implode while it was en route to visit the wreckage of the RMS in June 2023, killing all five passengers on board. But according to their findings, investigators noted that the submersible wasn't damaged shortly before its final voyage.

In high energy density physics (HEDP) and inertial confinement fusion (ICF), predictive modeling is complicated by uncertainty in parameters that characterize various aspects of the modeled system, such as those characterizing material properties, equation of state (EOS), opacities, and initial conditions. Typically, however, these parameters are not directly observable. What is observed instead is a time sequence of radiographic projections using X-rays. In this work, we define a set of sparse hydrodynamic features derived from the outgoing shock profile and outer material edge, which can be obtained from radiographic measurements, to directly infer such parameters. Our machine learning (ML)-based methodology involves a pipeline of two architectures, a radiograph-to-features network (R2FNet) and a features-to-parameters network (F2PNet), that are trained independently and later combined to approximate a posterior distribution for the parameters from radiographs. We show that the estimated parameters can be used in a hydrodynamics code to obtain density fields and hydrodynamic shock and outer edge features that are consistent with the data. Finally, we demonstrate that features resulting from an unknown EOS model can be successfully mapped onto parameters of a chosen analytical EOS model, implying that network predictions are learning physics, with a degree of invariance to the underlying choice of EOS model.

OpenAI was the hottest startup in Silicon Valley off the success of ChatGPT. Then, the board fired Sam Altman. If you enjoy this show, please consider signing up for Slate Plus. Slate Plus members get benefits like zero ads on any Slate podcast, bonus episodes of shows like Slow Burn and Dear Prudence--and you'll be supporting the work we do here on What Next TBD. Sign up now at slate.com/whatnextplus to help support our work.

Demand for high-end chips allows cryptocurrency companies to repurpose idle equipment

A video of a Coca-Cola being instantly crushed by water pressure gives an idea of what likely happened to the destroyed Titan submersible that claimed five lives. The pressure chamber on board OceanGate's 22-ft vessel suffered a'catastrophic implosion', authorities revealed on Thursday, and the men aboard tragically perished. The implosion would have been caused by a sudden change from low to high pressure inside the sub, possibly triggered by a defect in the sub's walls. Experts say the crew and passengers would have been crushed within a fraction of a millisecond, an event so fast it would have been over before they realized what was happening and so violent it's unlikely to leave anything but a mist of human remains. A video posted by James Hambley, a YouTuber who goes by the alias Barded Science Guy, shows a similar implosion happening to a Coke can.

Inertial confinement fusion (ICF) experiments on NIF are extremely complex and costly, and it is challenging to accurately and consistently predict the outcome. But that is now changing, thanks to the work of LLNL design physicists. In a paper recently published in Physics of Plasmas, design physicist Kelli Humbird and her colleagues describe a new machine learning-based approach for modeling ICF experiments that results in more accurate predictions of NIF shots. The paper reports that machine learning models that combine simulation and experimental data are more accurate than the simulations alone, reducing prediction errors from as high as 110 percent to less than 7 percent. "This paper on cognitive simulation models leverages a technique called'transfer learning,' " said Humbird, lead author of the paper, "that lets us combine our simulation knowledge and previous experimental data into a model that is more predictive of future inertial confinement fusion experiments at NIF than simulations alone."