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Reservoir computation models form a subclass of recurrent neural networks with fixed non-trainable input and dynamic coupling weights. Only the static readout from the state space (reservoir) is trainable, thus avoiding the known problems…
Nuclear reactors are uniquely powerful, abundant, and flavor-pure sources of antineutrinos that continue to play a vital role in the US neutrino physics program. The US reactor antineutrino physics community is a diverse interest group…
Solid-state modulators for pulsed power applications have been a goal since the first fast high-power semiconductor devices became available. Recent improvements in both the speed and peak power capabilities of semiconductor devices…
Fifty years of developments in nuclear magnetic resonance (NMR) have resulted in an unrivaled degree of control of the dynamics of coupled two-level quantum systems. This coherent control of nuclear spin dynamics has recently been taken to…
Flexible Electronics (FE) technology offers uniquecharacteristics in electronic manufacturing, providing ultra-low-cost, lightweight, and environmentally-friendly alternatives totraditional rigid electronics. These characteristics enable a…
The Nuclear Science References (NSR) database together with its associated Web interface, is the world's only comprehensive source of easily accessible low- and intermediate-energy nuclear physics bibliographic information for more than…
The Nuclear Science References (NSR) database together with its associated Web interface, is the world's only comprehensive source of easily accessible low- and intermediate-energy nuclear physics bibliographic information for more than…
Machine learning is becoming a new paradigm for scientific research in various research fields due to its exciting and powerful capability of modeling tools used for big-data processing task. In this mini-review, we first briefly introduce…
Nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI) are versatile tools with broad applications from physics and chemistry to geology and medical studies. In this mini-review, we consider the concepts of NMR and MRI…
While microprocessors are used in various applications, they are precluded from the use in high-energy physics applications due to the harsh radiation present. To overcome this limitation a microprocessor design must withstand high doses of…
The quantification of controllability and observability has recently received new interest in the context of large, complex networks of dynamical systems. A fundamental but computationally difficult problem is the placement or selection of…
Storage Class Memory (SCM) is a class of memory technology which has recently become viable for use. Their namearises from the fact that they exhibit non-volatility of data, similar to secondary storage while also having latencies…
The integration of renewable and distributed energy resources reshapes modern power systems, challenging conventional protection schemes. This scoping review synthesizes recent literature on machine learning (ML) applications in power…
Quantum reservoir computing employs fixed quantum dynamics as a feature map for machine learning. Integrating multiple quantum reservoirs, however, raises a key question: how few inter-module connections are sufficient to match the…
The supercritical, reactor core melting and nuclear fuel leaking accidents have troubled fission reactors for decades, and greatly limit their extensive applications. Now these troubles are still open. Here we first show a possible perfect…
SRAM-based FPGAs are popular in the aerospace industry for their field programmability and low cost. However, they suffer from cosmic radiation-induced Single Event Upsets (SEUs). Triple Modular Redundancy (TMR) is a well-known technique to…
Irradiation-induced void swelling is a critical degradation mechanism for structural materials in nuclear reactors, dictating component operational lifespan and safety. While recent machine learning (ML) approaches have improved the…
We present the construction of molecular force fields for small molecules (less than 25 atoms) using the recently developed symmetrized gradient-domain machine learning (sGDML) approach [Chmiela et al., Nat. Commun. 9, 3887 (2018); Sci.…
Amorphous materials are coming within reach of realistic computer simulations, but new approaches are needed to fully understand their intricate atomic structures. Here, we show how machine-learning (ML)-based techniques can give new,…
The reactor protection system (RPS), as a 1E-level safety system, should be designed and developed following a series of nuclear laws and technical disciplines.