80 件 ・ 関連度順
概要と使いどころ
Patterns for loading PDE simulation datasets (PDEBench, PhiFlow, JAX-CFD) from HDF5 files. Handles layout detection (single tensor vs separate variables), spatial/temporal downsampling, multi-variable systems, HuggingFace and DaRUS data sources, and efficient PyTorch DataLoader creation. Use when preparing PDE data for neural operator training.
日本語の概要は準備中です。原文の説明を表示しています。
Training patterns for autoregressive neural PDE solvers (FNO, DeepONet, CNO). Covers rollout training, noise injection for stability, multi-component loss functions (H1, frequency-sensitive, boundary-aware), per-channel normalization for coupled multi-variable systems, and the PDEBench nRMSE metric. Use when training any neural operator that predicts time-dependent PDE solutions.
日本語の概要は準備中です。原文の説明を表示しています。
End-to-end finite element analysis in MATLAB PDE Toolbox — geometry creation, model setup, solve, and post-processing in one skill. Use when building geometry from primitives or file import, setting up femodel with BCs/loads/materials, solving thermal/structural/EM problems, and extracting or visualizing results. Covers fegeometry, multicuboid, multicylinder, multisphere, decsg, boolean ops, mesh generation, femodel, all AnalysisTypes (thermalSteady, thermalTransient, structuralStatic, structuralTransient, structuralModal, structuralFrequency, electrostatic, magnetostatic, dcConduction, harmonic EM), materialProperties, faceBC, faceLoad, cellLoad, vertexLoad, solve, interpolation, von Mises stress, principal stress, reaction forces, heat flux, pdeplot3D visualization. Triggers on: PDE Toolbox, finite element, FEA, thermal analysis, structural analysis, electromagnetic analysis, femodel, mesh, boundary conditions, stress, displacement, heat transfer, post-processing.
日本語の概要は準備中です。原文の説明を表示しています。
AppDeploy Skill workflow skill. Use this skill when the user needs Deploy web apps with backend APIs, database, and file storage. Use when the user asks to deploy or publish a website or web app and wants a public URL. Uses HTTP API via curl and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
日本語の概要は準備中です。原文の説明を表示しています。
Train neural operators (FNO, DeepONet) to learn solution maps for parametric PDE families. Once trained, solve new PDE instances in milliseconds. Use when you need to solve many instances of the same PDE with different parameters/ICs/BCs.
日本語の概要は準備中です。原文の説明を表示しています。
AppDeploy Skill workflow skill. Use this skill when the user needs Deploy web apps with backend APIs, database, and file storage. Use when the user asks to deploy or publish a website or web app and wants a public URL. Uses HTTP API via curl and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
日本語の概要は準備中です。原文の説明を表示しています。
Frappe Helpdesk implementation and customization guidance for ticket intake, SLAs, assignment, portals, knowledge base, notifications, and support workflows. Use when work touches the Frappe Helpdesk app or customer support operations.
日本語の概要は準備中です。原文の説明を表示しています。
Generic helpdesk first-response skill for creating KB-based response drafts. Use when handling support tickets, creating response templates, or building a structured helpdesk workflow. Supports error code detection, keyword matching, confidence scoring, multi-language templates, and escalation workflows. Customize by providing your own KB articles and configuration.
日本語の概要は準備中です。原文の説明を表示しています。
Solve partial differential equations — finite differences, spectral methods, and physics-informed neural networks (PINNs via DeepXDE). Supports 1D/2D/3D, steady/transient, linear/nonlinear PDEs with Dirichlet, Neumann, and periodic boundary conditions.
日本語の概要は準備中です。原文の説明を表示しています。
Train Physics-Informed Neural Networks (PINNs) using DeepXDE. Solve forward and inverse PDE problems by embedding physics equations into the neural network loss function. Supports 1D/2D/3D, time-dependent, and parametric PDEs.
日本語の概要は準備中です。原文の説明を表示しています。
MNN QNN（高通 HTP/NPU）后端的问题定位、修复与算子适配。覆盖运行时报错（1002/6000/1003/6004）、模型转换/算子校验失败、推理结果异常、精度不达标、以及性能问题;并指导为 QNN 新增/适配算子。核心方法:用 MNN2QNNModel --dump_intermediate_outputs 一次性导出 QNN 全部中间张量,与 CPU(fwd=0) 基线逐张量对比,直接定位首个出错算子(旧的 testMNNFromOnnx.py 截断二分已降为回退手段);区分“真 bug/量化/HTP fp16 精度”;新增算子先查 SDK 算子文档(MasterOpDef/HtpOpDefSupplement)再实现。
日本語の概要は準備中です。原文の説明を表示しています。
Automate HelpDesk tasks via Rube MCP (Composio): list tickets, manage views, use canned responses, and configure custom fields. Always search tools first for current schemas.
日本語の概要は準備中です。原文の説明を表示しています。
Generate correct MATLAB code using the Symbolic Math Toolbox. Use when the user asks for symbolic computations, analytical solutions, symbolic differentiation/integration, equation solving, or converting symbolic results to numeric MATLAB functions. Also use when converting differential equations to transfer functions or state-space form, extracting PDE coefficients (pdeCoefficients), symbolic-to-C code generation (matlabFunction + codegen), symbolic matrix variables (symmatrix), physical units and constants (symunit, newUnit), or rewriting/combining algebraic expressions (rewrite, combine).
日本語の概要は準備中です。原文の説明を表示しています。
Expert EU Cyber Resilience Act (CRA) advisor for Regulation (EU) 2024/2847 — mandatory cybersecurity and vulnerability handling requirements for all products with digital elements (PDEs) sold in the EU. Use this skill for gap analysis, product classification (Default / Class I / Class II), conformity assessment route selection, CE marking, SBOM requirements, vulnerability and incident reporting to ENISA/CSIRTs, support period obligations, and manufacturer/importer/distributor duties. Trigger for EU CRA, Cyber Resilience Act, PDE compliance, Annex I requirements, SBOM EU, CE marking cybersecurity, or connected product security EU.
日本語の概要は準備中です。原文の説明を表示しています。
Expert EU Cyber Resilience Act (CRA) advisor for Regulation (EU) 2024/2847 — mandatory cybersecurity and vulnerability handling requirements for all products with digital elements (PDEs) sold in the EU. Use this skill for gap analysis, product classification (Default / Class I / Class II), conformity assessment route selection, CE marking, SBOM requirements, vulnerability and incident reporting to ENISA/CSIRTs, support period obligations, and manufacturer/importer/distributor duties. Trigger for EU CRA, Cyber Resilience Act, PDE compliance, Annex I requirements, SBOM EU, CE marking cybersecurity, or connected product security EU.
日本語の概要は準備中です。原文の説明を表示しています。
Master the AI tools that transform customer support from cost center to competitive advantage. Automate routine queries, empower agents, and delight customers at scale. Use when "customer support, helpdesk, ticketing system, chatbot, knowledge base, support automation, CSAT, customer service, support, helpdesk, chatbot, knowledge-base, customer-service, automation" mentioned.
日本語の概要は準備中です。原文の説明を表示しています。
全国大学生数学建模竞赛（CUMCM）全流程参赛助手。覆盖 72 小时赛程的读题选题、数据探索、模型选择与求解、论文写作、格式检查、支撑材料打包；A 类机理/PDE/传热传质题按 22-mechanism-pde 走单位字典、守恒与网格收敛。Use when 用户参加数学建模竞赛/国赛、拿到赛题需要分析建模、需要选模型写代码、需要按国赛规范写建模论文/摘要、需要检查论文格式或准备提交、做烘干/炉温/传热传质/有限体积。与美赛无关的普通代码任务不要触发。
日本語の概要は準備中です。原文の説明を表示しています。
Complaint/ticket triage-and-dispatch App-in-Skill (Busabase App-in-Skill) for property managers, facilities teams, and helpdesk leads. Use when the user invokes $kelly-tickets or /kelly-tickets, or asks for complaint triage, ticket dispatch, 投诉, 工单, 派单, property management complaints, helpdesk queue, intake from WeChat exports/call logs/front-desk forms, crew assignment, SLA board, dispatch approval, or ticket resolution tracking.
日本語の概要は準備中です。原文の説明を表示しています。
Architectures and techniques for neural operators on discontinuous PDE solutions (shocks, contact discontinuities, steep gradients). Covers local-global spectral design (ShockFNO), reflection padding for non-periodic BCs, resolution scaling for shock width, and frequency-band error diagnostics. Use for low-viscosity Burgers, compressible Euler, Riemann problems, or any PDE where standard FNO produces Gibbs oscillations.
日本語の概要は準備中です。原文の説明を表示しています。
Discovers and evaluates scientific datasets, models, methodology posts, and Spaces through the Hugging Science catalog. Used when selecting scientific ML resources in biology, chemistry, genomics, materials, climate, physics, astronomy, medicine, mathematics, protein design, single-cell analysis, or PDE modeling, and when checking their actual datasets, Transformers, native-runtime, Inference Providers, or Gradio interfaces.
日本語の概要は準備中です。原文の説明を表示しています。
Deploy web apps with backend APIs, database, and file storage. Use when the user asks to deploy or publish a website or web app and wants a public URL. Uses HTTP API via curl.
日本語の概要は準備中です。原文の説明を表示しています。
Problem-solving strategies for second order odes in odes pdes
日本語の概要は準備中です。原文の説明を表示しています。
Problem-solving strategies for boundary value problems in odes pdes
日本語の概要は準備中です。原文の説明を表示しています。
Problem-solving strategies for first order odes in odes pdes
日本語の概要は準備中です。原文の説明を表示しています。