Results 161 to 170 of about 149,590 (309)

Estimation of Default Probabilities Part 2: Market Factor Based Techniques

, 2003
Uwe Wehrspohn
openalex   +1 more source

Sequential Monte Carlo with likelihood tempering and parallel implementation for uncertainty quantification

AIChE Journal, EarlyView.
Abstract Bayesian estimation enables uncertainty quantification, but analytical implementation is often intractable. As an approximate approach, the Markov Chain Monte Carlo (MCMC) method is widely used, though it entails a high computational cost due to frequent evaluations of the likelihood function.
Tatsuki Maruchi, Tomoyuki Yajima, Yoshiaki Kawajiri   +2 more
wiley   +1 more source

AI in chemical engineering: From promise to practice

AIChE Journal, EarlyView.
Abstract Artificial intelligence (AI) in chemical engineering has moved from promise to practice: physics‐aware (gray‐box) models are gaining traction, reinforcement learning complements model predictive control (MPC), and generative AI powers documentation, digitization, and safety workflows.
Jia Wei Chew, Ronnie Andersson, Thomas Bierweiler, Patrik Ryttestal, Torsten Wik   +4 more
wiley   +1 more source

Hyperparameter optimization to enhance the performance of deep learning models for the early detection of invasive turtles in Korea. [PDF]

Sci Rep
Baek JW, Kim JI, Mun MH, Kim CB.
europepmc   +1 more source

Smes and Sustainable Finance: How Environmental Performance Influences the Default Probability and the Cost of Capital

, 2023
Egidio Palmieri, Federico Beltrame, Gianni Zorzi, Enrico Geretto   +3 more
openalex   +1 more source

Universally Accurate or Specifically Inadequate? Stress‐Testing General Purpose Machine Learning Interatomic Potentials

Advanced Intelligent Discovery, EarlyView.
We investigate MACE‐MP‐0 and M3GNet, two general‐purpose machine learning potentials, in materials discovery and find that both generally yield reliable predictions. At the same time, both potentials show a bias towards overstabilizing high energy metastable states. We deduce a metric to quantify when these potentials are safe to use.
Konstantin S. Jakob, Karsten Reuter, Johannes T. Margraf   +2 more
wiley   +1 more source

Imprecision in tuberculosis infection outcomes: implications for non-inferiority vaccine trials. [PDF]

Int J Epidemiol
Grint DJ, White RG, Churchyard G, Fiore-Gartland A, Rangaka M, Garcia-Basteiro AL, Cobelens F.   +6 more
europepmc   +1 more source

Bayesian Optimization Guiding the Experimental Mapping of the Pareto Front of Mechanical and Flame‐Retardant Properties in Polyamide Nanocomposites

Advanced Intelligent Discovery, EarlyView.
Bayesian optimization enabled the design of PA56 system with just 8 wt% additives, achieving limiting oxygen index 30.5%, tensile strength 80.9 MPa, and UL‐94 V‐0 rating. Without prior knowledge, the algorithm uncovered synergistic effects between aluminum diethyl‐phosphinate and nanoclay.
Burcu Ozdemir, Miguel Hernández‐del‐Valle, Christina Schenk, De‐Yi Wang, Maciej Haranczyk   +4 more
wiley   +1 more source

DREAM-Stellar: parallel and space efficient exact local alignment. [PDF]

BMC Bioinformatics
Aasna E, Gottlieb SG, Ehrhardt M, Reinert K.   +3 more
europepmc   +1 more source

Customer Complaining and Probability of Default in Consumer Credit

, 2018
Stefano Cosma, Francesca Pancotto, Paola Vezzani   +2 more
openalex   +2 more sources