Results 101 to 110 of about 195,667 (268)
Bayesian inverse problems with Monte Carlo forward models
The full application of Bayesian inference to inverse problems requires exploration of a posterior distribution that typically does not possess a standard form. In this context, Markov chain Monte Carlo (MCMC) methods are often used.
Langmore, Ian +2 more
core +1 more source
The article overviews past and current efforts on caloric materials and systems, highlighting the contributions of Ames National Laboratory to the field. Solid‐state caloric heat pumping is an innovative method that can be implemented in a wide range of cooling and heating applications.
Agata Czernuszewicz +5 more
wiley +1 more source
Isospectral sets and inverse problems for vector-valued Sturm-Liouville equations
In this paper, we investigate inverse spectral problems for vectorial Sturm–Liouville equations via the matrix-valued Gelfand–Levitan equation. With this approach, we prove some uniqueness theorems for the even problem, mixed data problem and interior ...
謝忠村; Shieh, Chung-tsun
core +1 more source
Advanced Design for Weakly Coupled Resonators by Automatic Active Optimization
An Automatic Active Optimization (AAO) strategy integrates machine learning predictors and genetic algorithms in a closed‐loop workflow. By iteratively expanding its dataset with new discoveries, AAO overcomes the limits of conventional methods. This approach finds superior microstructural designs beyond the initial sample space. We demonstrate this on
Wei Yue +8 more
wiley +1 more source
This review explores advances in wearable and lab‐on‐chip technologies for breast cancer detection. Covering tactile, thermal, ultrasound, microwave, electrical impedance tomography, electrochemical, microelectromechanical, and optical systems, it highlights innovations in flexible electronics, nanomaterials, and machine learning.
Neshika Wijewardhane +4 more
wiley +1 more source
Transducers convert physical signals into electrical and optical representations, yet each mechanism is bounded by intrinsic trade‐offs across bandwidth, sensitivity, speed, and energy. This review maps transduction mechanisms across physical scale and frequency, showing how heterogeneous integration and multiphysics co‐design transform isolated ...
Aolei Xu +8 more
wiley +1 more source
This study explores how machine learning models, trained on small experimental datasets obtained via Phase Doppler Anemometry (PDA), can accurately predict droplet size (D32) in ultrasonic spray coating (USSC). By capturing the influence of ink complexity (solvent, polymer, nanoparticles), power, and flow rate, the model enables precise droplet control
Pieter Verding +5 more
wiley +1 more source
ABSTRACT Molybdenum disulfide (MoS2) has attracted attention as a promising material due to the growing demand for environmentally friendly, cost‐effective, and efficient water treatment techniques. With its physicochemical characteristics, this stratified bidimensional material allows it to be highly effective in adsorption and catalytic performance ...
Pariksha Bishnoi +4 more
wiley +1 more source
Generalized Bayes approach to inverse problems with model misspecification. [PDF]
Baek Y, Aquino W, Mukherjee S.
europepmc +1 more source
A stress‐normalised sensitivity metric (S = G/Y) is introduced as a materials‐level benchmark for intrinsically piezoresistive nanocomposites. By decoupling electromechanical response (G) from stiffness (Y), the framework enables direct comparison across diverse systems and clarifies design trade‐offs for wearable sensors.
Conor S. Boland
wiley +1 more source

