Nonlinear mixed effects models (NLMMs) and self-modeling nonlinear regression (SEMOR) models are often used to fit repeated measures data. They use a common function shared by all subjects to model ...

Materials scientists can learn a lot about a sample material by shooting lasers at it. With nonlinear optical microscopy—a ...

This article reviews work on the planning of experiments with response functions nonlinear in some parameters. Apart from older work on dilution series experiments and quantal bioassays, this field is ...

The study departs from conventional mean-based economic forecasting by focusing on quantile prediction, a technique that ...

We leverage recent advancements in machine learning to develop an integrated method to solve globally and estimate models featuring agent heterogeneity, nonlinear constraints, and aggregate ...

Ryotatsu Yanagimoto, a scientist at NTT Research, led the research under the supervision of Peter L. McMahon, an associate professor at Cornell University. The paper, " Programmable On-Chip Nonlinear ...

From industrial robots to self-driving cars, engineers face a common problem: keeping machines steady and predictable. When systems move, they can spiral out of control unless designed with ...

Activation functions play a critical role in AI inference, helping to ferret out nonlinear behaviors in AI models. This makes them an integral part of any neural network, but nonlinear functions can ...

Highly reduced order models with nonlinear contact – extremely efficient computation. Modeled as linear components interconnected with highly reduced-order components. 1-linear reduced model, ...

Effectiveness of Adjuvant Chemotherapy After Radical Nephroureterectomy for Locally Advanced and/or Positive Regional Lymph Node Upper Tract Urothelial Carcinoma This analysis included 502,321 ...