Microfluidics Platforms for High Throughput In Vivo Screening
Explore microfluidic approaches for high-throughput phenotypic screening in C. elegans, offering human-relevant biology and addressing limitations of in vitro models in modern drug discovery.
Droplet Microfluidics in PCR: Technology and Applications
Explore droplet microfluidics in PCR, focusing on Digital Droplet PCR technology, its advantages over traditional qPCR, and applications in various fields like NIPT, pathogen detection, and liquid biopsy.
Linking Single-Cell Function to Multi-omic Analysis Using 'Lab on a Particle' Technology
Explore innovative 'Lab on a Particle' technology for single-cell analysis, linking cell function to multi-omic data. Learn about hydrogel microparticles for cell capture and secretion analysis in various applications.
中性子イメージングの概要 - チャプター5
中性子イメージング技術の基礎と応用について学び、流体の可視化や物質内部の非破壊観察手法を理解する実践的な講義。
中性子で視る流れ - チャプター7
中性子を用いた流体の可視化技術について学び、最新の研究成果と応用例を通じて流体力学の新しい観測方法を理解する。
Distributed Mesh Processing and Geometric Deep Learning for Wind Prediction
Discover how Ray's distributed computing powers real-time wind prediction in urban design through mesh processing, geometric deep learning, and CFD data analysis for architectural innovation.
Five Ways to Understand Kubernetes Networking for Performance-Intensive Workloads
Explore Kubernetes networking innovations for high-performance workloads. Compare CNI implementations using real-world benchmarks to analyze performance in scientific computing and machine learning.
Meta-Learning Dynamics Forecasting Using Task Inference
Explore meta-learning dynamics forecasting using task inference to improve generalization across heterogeneous domains in deep learning models for predicting complex systems.
Machine Learning for Computational Fluid Dynamics
Explore machine learning's potential to enhance computational fluid dynamics, covering applications in simulations, turbulence modeling, and reduced-order models while emphasizing physics-informed approaches.
Deep Learning for Turbulence Closure Modeling
Explore how deep learning revolutionizes turbulence modeling in fluid dynamics, focusing on RANS equations and large eddy simulations for advanced computational techniques.
Machine Learning for Fluid Dynamics: Models and Control
Explore machine learning applications in fluid dynamics modeling and control, covering patterns, complexity, RANS closure models, SINDY, and deep MPC for flow control.
Machine Learning for Fluid Dynamics - Patterns
Explore machine learning applications in fluid dynamics, focusing on pattern extraction and coherent structure identification in high-dimensional systems.
Blender Tutorial - Fire Particle Animation
Learn to create realistic fire particle animations in Blender 2.90 using Mantaflow and the Particle System. Master fluid flow force fields to make particles follow smoke for stunning visual effects.
Universality Classes for 1+1 Dimensional Systems
Explore universality classes in 1+1 dimensional systems, focusing on weak universality conjecture, graphical construction, and links to Brownian web. Gain insights into complex systems modeling.
Interpretable and Generalizable Machine Learning for Fluid Dynamics - Steven Brunton
Explore interpretable and generalizable machine learning techniques for fluid dynamics, focusing on sparse identification of nonlinear dynamics and incorporating physical constraints for improved accuracy.
Physics-Guided Deep Learning for Fluid Dynamics - Rose Yu
Explores physics-guided deep learning for fluid dynamics, focusing on accelerating turbulence simulations and incorporating symmetry for generalization in ocean current forecasting.
The Deep Learning - Applied Math Connection
Explore the intersection of deep learning and applied mathematics, focusing on advancements in signal processing, optimization, and self-supervised learning for predictive modeling.
Machine Learning for Analysis of High-Dimensional Spatiotemporal Chaotic Dynamical Systems
Discover how machine learning techniques can analyze and predict complex chaotic systems, with insights from cutting-edge research in physics and data science.
Machine Learning in Fluid Dynamics and Climate Physics
Explore machine learning applications in fluid dynamics and climate modeling, including physics-informed approaches, data integration, and simulation validation techniques.
Coding Adventure: Simulating Fluids
Explore particle-based fluid simulation techniques, from basic physics to advanced concepts like smoothed particles, density calculation, and pressure forces, with practical coding examples and optimizations.
On Random Grid Neural Processes for Solving Forward and Inverse Problems
Explore innovative deep learning models for parametric PDEs using random grids and variational neural processes, addressing challenges in physics-informed frameworks and data integration.