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関連する概念動画

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements10:22

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

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This article presents a protocol and software tool for the quantification of uncertainties in the calibration and data analysis of a semi-continuous thermal-optical organic/elemental carbon...
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The Uncertainty Principle04:08

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Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...
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Uncertainty in Measurement: Reading Instruments02:46

Uncertainty in Measurement: Reading Instruments

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Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
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Uncertainty: Overview00:59

Uncertainty: Overview

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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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To date research has focused on cognitive strategies people adopt to cope with uncertainty. This research examines instead an experiential way of dealing with uncertainty and introduces a set of experimental methods showing how the experience of haptic softness can serve as a tool to deal with...
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Mime is a flexible computational framework to construct a machine learning-based integration model with elegant performance. Here, we provide a detailed step-by-step procedure for developing predictive models with high accuracy, leveraging complex datasets to identify critical genes associated with disease progression, patient outcomes, and therapeutic response.
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Updated: Jan 20, 2026

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
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VENI, VINDy, VICI：不確実性定量化を備えた生成縮小順序モデリングフレームワーク

Paolo Conti1, Jonas Kneifl2, Andrea Manzoni1

  • 1MOX - Department of Mathematics, Politecnico di Milano, Milan, Italy.

Neural networks : the official journal of the International Neural Network Society
|January 18, 2026
PubMed
まとめ
この要約は機械生成です。

物理的に一貫した科学的予測を保証する生成モデルの新しいフレームワークを導入します。このアプローチは、データ駆動型メソッドと確率的モデリングを組み合わせて、正確で不確実性を考慮した縮小順序モデルを作成します。

キーワード:
データ駆動型メソッド生成AI非線形ダイナミクス縮小順序モデリングスパースシステム同定変分オートエンコーダー

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関連する実験動画

Last Updated: Jan 20, 2026

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科学分野:

  • 計算科学
  • 物理情報機械学習
  • データ駆動型モデリング

背景:

  • 生成モデルは効率的なシナリオ探索を提供しますが、物理的な一貫性が欠けていることがよくあります。
  • 計算科学は、信頼性の高い予測のために物理的な一貫性に依存しています。
  • 既存のモデルは、データ駆動型の洞察と物理法則のバランスをとるのに苦労しています。

研究 の 目的:

  • 物理的に一貫した縮小順序モデルを作成するための新しい物理的生成フレームワークを開発すること。
  • 不確実性定量化のための確率的モデリングとデータ駆動型システム同定を統合すること。
  • モデルの信頼性を確保することにより、複雑な物理現象における意思決定を強化すること。

主な方法:

  • VENI（ノイズ入力の変分エンコーディング）：高次元でノイズの多いデータから縮小座標を識別するために変分オートエンコーダーを利用します。
  • VINDy（非線形ダイナミクスの変分同定）：システムダイナミクスを発見するために確率的モデリングでスパースシステム同定を拡張します。
  • VICI（信頼区間による変分推論）：完全時間解の効率的な生成を可能にし、不確実性定量化を提供します。

主要な成果:

  • 提案されたフレームワークは、物理的に一貫した縮小順序モデルを正常に構築します。
  • 未知のパラメータと初期条件に対する効果的な不確実性定量化を実証しました。
  • カオスおよび高次元の非線形ダイナミクスを含む多様なシステム全体でパフォーマンスを検証しました。

結論:

  • VENI、VINDy、VICIフレームワークは、物理的に一貫した生成モデリングのための堅牢なソリューションを提供します。
  • このアプローチは、科学および工学における生成モデルの信頼性と適用性を高めます。
  • これにより、複雑な物理システムの、より信頼性が高く効率的な計算的探索への道が開かれます。