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Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

41
Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
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Chemical Equilibria: Systematic Approach to Equilibrium Calculations01:21

Chemical Equilibria: Systematic Approach to Equilibrium Calculations

628
Equilibrium calculations for systems involving multiple equilibria are often complex. For example, to calculate the solubility of a sparingly soluble salt in an aqueous solution in the presence of a common ion, one must consider all the equilibria in this solution. Calculations for these systems can be complicated and tedious, so a systematic approach with a series of steps is often helpful. The process is detailed below.
The first step is to identify all the chemical reactions involved, The...
628
Chemical Equations03:10

Chemical Equations

62.7K
Chemical equations represent the identities and relative quantities of substances involved in a chemical reaction. The substances undergoing reaction are called reactants, and their formulas are placed on the left side of the equation. The substances generated by the reaction are called products, and their formulas are placed on the right side of the equation. Plus signs (+) separate individual reactant and product formulas, and an arrow (→) separates the reactant and product (left and...
62.7K
Homogeneous Equilibria for Gaseous Reactions02:15

Homogeneous Equilibria for Gaseous Reactions

24.6K
Homogeneous Equilibria for Gaseous Reactions
For gas-phase reactions, the equilibrium constant may be expressed in terms of either the molar concentrations (Kc) or partial pressures (Kp) of the reactants and products. A relation between these two K values may be simply derived from the ideal gas equation and the definition of molarity. According to the ideal gas equation:
24.6K
The Nernst Equation02:59

The Nernst Equation

40.2K
Nonstandard Reaction Conditions
The interconnection between standard cell potentials and various thermodynamic parameters such as the standard free energy change ΔG° and equilibrium constant K has been previously explored. For example, a redox reaction involving zinc(II) and tin(II) ions at 1 M concentration with Eºcell = +0.291 V and ΔG° = −56.2 kJ is spontaneous.
40.2K

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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
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Neural network emulator for atmospheric chemical ODE.

Zhi-Song Liu1, Petri Clusius2, Michael Boy3

  • 1School of Engineering Sciences, Lappeenranta-Lahti University of Technology LUT, Lahti, 15110, Finland; Atmospheric Modelling Centre Lahti, Lahti University Campus, Lahti, 15140, Finland.

Neural Networks : the Official Journal of the International Neural Network Society
|January 5, 2025
PubMed
Summary

We developed ChemNNE, a neural network emulator, to rapidly model atmospheric chemistry. This approach significantly improves computational speed and accuracy for predicting chemical concentrations.

Keywords:
Atmospheric chemistryAttentionAutoencoderNeural networkSurrogate

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Area of Science:

  • Atmospheric Chemistry
  • Computational Science
  • Artificial Intelligence

Background:

  • Atmospheric chemistry modeling is computationally intensive.
  • Deep neural networks show promise in complex modeling tasks.

Purpose of the Study:

  • To develop a fast and accurate neural network emulator for atmospheric chemistry.
  • To model atmospheric chemistry as a time-dependent Ordinary Differential Equation (ODE) using neural networks.

Main Methods:

  • Proposed ChemNNE, an Attention-based Neural Network Emulator (NNE).
  • Utilized sinusoidal time embedding for temporal patterns and Fourier neural operator for ODE modeling.
  • Introduced three physics-informed loss functions for training.

Main Results:

  • Achieved state-of-the-art performance in modeling accuracy.
  • Demonstrated significant improvements in computational speed.
  • Introduced a novel large-scale dataset for benchmarking.

Conclusions:

  • ChemNNE offers an efficient and accurate solution for atmospheric chemistry modeling.
  • The physics-informed approach enhances model reliability.
  • The developed dataset facilitates future research in the field.