Lifeboat Foundation

At some point, theoretical physics shades into science fiction. This is a beautiful little book, by a celebrated physicist and writer, about a phenomenon that is permitted by equations but might not actually exist. Or perhaps white holes do exist, and are everywhere: we just haven’t noticed them yet. No such controversy exists about black holes, wh…

Authors: Tianyi Liang, Dong Wu, Xiaochuan Ning, Lianqiang Shan, Zongqiang Yuan, Hongbo Cai, Zhengmao Sheng, and Xiantu He. Discover more in PRE:

The National Ignition Facility has recently achieved successful burning plasma and ignition using the inertial confinement fusion (ICF) approach. However, there are still many fundamental physics phenomena that are not well understood, including the kinetic processes in the hohlraum. Shan et al. [Phys. Rev. Lett. 120, 195001 (2018)] utilized the energy spectra of neutrons to investigate the kinetic colliding plasma in a hohlraum of indirect drive ICF. However, due to the typical large spatial-temporal scales, this experiment could not be well simulated by using available codes at that time. Utilizing our advanced high-order implicit PIC code, LAPINS, we were able to successfully reproduce the experiment on a large scale of both spatial and temporal dimensions, in which the original computational scale was increased by approximately seven to eight orders of magnitude.

Pioneering work in laser physics has laid the foundation for significant advancements in precision measurement, enabling the development of techniques that significantly reduce residual amplitude modulation.

Within atomic and laser physics communities, scientist John “Jan” Hall is a key figure in the history of laser frequency stabilization and precision measurement using lasers. Hall’s work revolved around understanding and manipulating stable lasers in ways that were revolutionary for their time. His work laid a technical foundation for measuring a tiny fractional distance change brought by a passing gravitational wave. His work in laser arrays awarded him the Nobel Prize in Physics in 2005.

Building on this foundation, JILA and NIST Fellow Jun Ye and his team embarked on an ambitious journey to push the boundaries of precision measurement even further. This time, their focus turned to a specialized technique known as the Pound-Drever-Hall (PDH) method (developed by scientists R. V. Pound, Ronald Drever, and Jan Hall himself), which plays a large role in precision optical interferometry and laser frequency stabilization.

Like Brian Greer has said the casimir technologies can power anything and create a free society a free utopia without the need for using any chemicals and it has been known since the 1950s in the physics community.

Previous demonstrations of the elusive Casimir force between interfaces exhibit monotonic dependence on surface displacement. Now a non-monotonic dependence of the force has been shown experimentally by exploting nanostructured surfaces.

Researchers analyzed emission data from quasar 3C 273 using two theoretical models, revealing complexities in understanding quasar behavior and the mechanics of supermassive black holes.

In a new paper in The Astrophysical Journal, JILA Fellow Jason Dexter, graduate student Kirk Long, and other collaborators compared two main theoretical models for emission data for a specific quasar, 3C 273. Using these theoretical models, astrophysicists like Dexter can better understand how these quasars form and change over time.

Quasars, or active galactic nuclei (AGN), are believed to be powered by supermassive black holes at their centers. Among the brightest objects in the universe, quasars emit a brilliant array of light across the electromagnetic spectrum. This emission carries vital information about the nature of the black hole and surrounding regions, providing clues that astrophysicists can exploit to better understand the black hole’s dynamics.

Models of systems in physics usually start with elementary processes. New work with a neural network shows how models can also be built by observing the system as a whole and deducing the underlying interactions.

This video shows the fly model reproducing a flight maneuver (spontaneous turning) of a real fly, executing commands to walk at a speed of 2 cm/s while turning left and right, and the model imitating a walking trajectory of the real fruit fly, which includes walking at different speeds, turning and briefly stopping. Credit: Vaxenburg et al.

By infusing a virtual fruit fly with artificial intelligence, Janelia and Google DeepMind scientists have created a computerized insect that can walk and fly just like the real thing.

Continue reading “Artificial intelligence brings a virtual fly to life” »

Their AI is able to recognize patterns in complex data sets and to formulate them in a physical theory. The development of a new theory is typically associated with the greats of physics. You might think of Isaac Newton or Albert Einstein, for example. Many Nobel Prizes have already been awarded for new theories. Researchers at Forschungszentrum Jülich have now programmed an artificial intelligence that has also mastered this feat. Their AI is able to recognize patterns in complex data sets and to formulate them in a physical theory.

In the following interview, Prof. Moritz Helias from Forschungszentrum Jülich’s Institute for Advanced Simulation (IAS-6) explains what the “Physics of AI” is all about and to what extent it differs from conventional approaches.

Fly, goat, fly! A new AI agent from Google DeepMind can play different games, including ones it has never seen before such as Goat Simulator 3, a fun action game with exaggerated physics. Researchers were able to get it to follow text commands to play seven different games and move around in three different 3D research environments. It’s a step toward more generalized AI that can transfer skills across multiple environments.

Google DeepMind has had huge success developing game-playing AI systems. Its system AlphaGo, which beat top professional player Lee Sedol at the game Go in 2016, was a major milestone that showed the power of deep learning. But unlike earlier game-playing AI systems, which mastered only one game or could only follow single goals or commands, this new agent is able to play a variety of different games, including Valheim and No Man’s Sky. It’s called SIMA, an acronym for “scalable, instructable, multiworld agent.”