Visualized with neural network icons Prompts

**“A futuristic neural network visualization with the Zapier logo glowing at the center as the core node. From the center, dozens of interconnected nodes and lines extend outward, representing popular apps and services connected to Zapier. Surrounding nodes subtly display recognizable app icons and UI-style symbols for tools such as Gmail, Google Sheets, Slack, Notion, Airtable, Stripe, Shopify, Webflow, WordPress, HubSpot, Salesforce, Trello, Asana, ClickUp, Calendly, Zoom, Typeform, OpenAI, Dropbox, GitHub, Discord, Twitter (X), Instagram, Facebook, LinkedIn, YouTube, Mailchimp, ActiveCampaign, SendGrid, Stripe, PayPal, Stripe, WooCommerce, Monday.com, Figma, Canva, Reddit, RSS feeds, and APIs. The style is clean, high-tech, and cinematic, inspired by neural networks and data flows, with glowing lines, soft gradients, and a dark or deep blue background. The Zapier logo is sharp, centered, and dominant, symbolizing automation and orchestration. Ultra-detailed, modern SaaS aesthetic, 3D depth, subtle motion blur, high contrast, professional tech illustration, 4K resolution.

Scientific infographic illustrating an AI-driven closed-loop framework for virtual molecular library construction, showing the adaptive cycle of “Representation – Generation – Prediction – Feedback”. Central theme: artificial intelligence empowering drug discovery and molecular design. The diagram is a circular workflow structure centered on the AI virtual molecular library system. Left module: Representation Learning, visualized with neural network icons, molecular graphs, protein structures, and amino acid sequence symbols, representing molecular and protein feature embeddings. Upper-right module: Molecular Generation, showing diffusion or VAE-like model generating diverse small molecules, arrows indicating exploration of chemical space, novelty, and synthesizability constraints. Lower-right module: Property Prediction, containing ADMET, activity, and selectivity metrics represented by radar charts or data panels, feeding results back to the representation module to close the loop. Bottom section: Evolution from virtual to drug-like molecular libraries, shown as a smooth gradient arrow with multi-objective optimization icons balancing drug-likeness and diversity. Right-side branch: Pretrained models for new target ligand design, divided into three submodules—small molecule pretraining, protein pretraining, and cross-modal pretraining (protein–ligand interaction)—depicting embedding fusion or contrastive learning in shared latent space. No human figures, only abstract scientific symbols and molecular visuals. Style: flat vector scientific infographic, modern and minimalistic, clear logical flow, smooth connections between modules. Color scheme: blue for AI and representation, orange-yellow for generation, green for prediction; background light gray or white. Typography: clean sans-serif labels, concise annotations. High resolution (≥600 dpi), suitable for journal publication, ultra-clear, balanced layout, professional academic tone.

: A 3D illustration of a neural network, with transparent nodes and glowing connections, showcasing the varying weights as different thicknesses and colors of the connecting lines.

: A 3D illustration of a neural network, with transparent nodes and glowing connections, showcasing the varying weights as different thicknesses and colors of the connecting lines.

Artificial neuron vector art.

Scientific infographic illustrating an AI-driven closed-loop framework for virtual molecular library construction, showing the adaptive cycle of “Representation – Generation – Prediction – Feedback”. Central theme: artificial intelligence empowering drug discovery and molecular design. The diagram is a circular workflow structure centered on the AI virtual molecular library system. Left module: Representation Learning, visualized with neural network icons, molecular graphs, protein structures, and amino acid sequence symbols, representing molecular and protein feature embeddings. Upper-right module: Molecular Generation, showing diffusion or VAE-like model generating diverse small molecules, arrows indicating exploration of chemical space, novelty, and synthesizability constraints. Lower-right module: Property Prediction, containing ADMET, activity, and selectivity metrics represented by radar charts or data panels, feeding results back to the representation module to close the loop. Bottom section: Evolution from virtual to drug-like molecular libraries, shown as a smooth gradient arrow with multi-objective optimization icons balancing drug-likeness and diversity. Right-side branch: Pretrained models for new target ligand design, divided into three submodules—small molecule pretraining, protein pretraining, and cross-modal pretraining (protein–ligand interaction)—depicting embedding fusion or contrastive learning in shared latent space. No human figures, only abstract scientific symbols and molecular visuals. Style: flat vector scientific infographic, modern and minimalistic, clear logical flow, smooth connections between modules. Color scheme: blue for AI and representation, orange-yellow for generation, green for prediction; background light gray or white. Typography: clean sans-serif labels, concise annotations. High resolution (≥600 dpi), suitable for journal publication, ultra-clear, balanced layout, professional academic tone.

A vertical banner for the Data Science Club at NIST University, professional and modern university design. NIST University logo placed at the top center (official look, flat vector style, correct proportions). Clean, tech-inspired background with data science elements such as graphs, charts, machine learning symbols, neural networks, AI icons, coding visuals, and abstract data flows. Color theme: navy blue, white, cyan, and dark gray with subtle gradients. Bold modern typography with the main title: “Data Science Club” Subtext below: “NIST University” Minimal, futuristic academic style, well-balanced spacing from top to bottom. High-resolution, print-ready, suitable for campus standees, roll-up banners, and posters.

Intertwined Neurons, complexity, iridescent fractal patterns, dark negative, reflections, volumetric lighting, cinematic, ultra sharp, ultra detailed, ultra realistic, HDR, octane rendering 3d + --ar 9:16, mdjrny-v4 style

Create a BioRender-style vector infographic. Place the panel header “研究目标” as a small caption in the upper-left corner (not a large title). Set its font size to ~80% of the main box titles, use regular weight (not bold), and keep it visually subtle. Use a clean professional layout, flat colors, thick outlines, minimal shadows, and consistent sans-serif font (Microsoft YaHei). Canvas: 16:9 or 16:10 landscape. The figure should be non-technical (no equations), focusing on goals hierarchy. All text inside boxes must be Chinese exactly as provided. Layout Split the figure into two parts: Left Part (Overall Goals Pyramid): Draw a 3-layer stacked pyramid (or three stacked rounded rectangles) labeled from top to bottom: “理论目标”, “方法目标”, “应用目标”. Use subtle distinct colors for each layer. Add a small left-side label “总体目标” above the pyramid. Right Part (Three Specific Objectives Cards): Place three numbered rounded-rectangle cards vertically aligned (G1, G2, G3). Each card contains the specific objective text. Draw thin arrows from each card to the related pyramid layer(s): G1 arrows to “理论目标” (primary) and slightly to “方法目标” (secondary) G2 arrows to “方法目标” (primary) and slightly to “理论目标” (secondary) G3 arrows to “应用目标” (primary) and slightly to “方法目标” (secondary) Bottom Bar (One-line Summary): Add a wide rounded rectangle at the bottom spanning the width, labeled “预期形成：低通信、高可靠、可验证的一致性控制理论与方法体系”. Icons (small, minimal, optional) Next to “理论目标”: a Lyapunov/analysis icon (V(x)) Next to “方法目标”: a neural network + event-trigger clock icon Next to “应用目标”: drone + robot icons Next to bottom bar: a balance scale icon labeled “性能—通信—能耗” Chinese text to place (exact) Overall Goals (left pyramid): Top layer title: “理论目标” Text: “建立一致性误差动力学描述与统一的收敛/有界性分析框架；给出误差判据、误差上界、无Zeno与IET下界等可验证结论。” Middle layer title: “方法目标” Text: “提出以DRNN为核心的学习辨识器、动态事件触发机制与分布式控制协议协同设计方法；揭示触发参数、拓扑、辨识误差与一致性性能的定性—定量关系；建立‘一致性性能—通信资源与能耗开销’权衡机制。” Bottom layer title: “应用目标” Text: “依托多无人机与多机器人平台开展仿真与实验验证；形成可推广的低通信、高可靠、可验证协同控制方法；服务无人系统集群、网络化制造单元及分布式能源等场景。” Right cards (specific objectives): Card 1 header: “具体目标1（G1）” Body: “构造显式利用触发区间信息的looped-functional型Lyapunov分析工具，降低收敛性与有界性结论的保守性；推导误差上界、无Zeno条件与IET下界；刻画IET与拓扑结构、触发参数及系统状态之间的定性—定量关系。” Card 2 header: “具体目标2（G2）” Body: “建立基于DRNN学习辨识的分布式事件触发一致性控制框架；研究学习误差、触发误差与拓扑耦合误差对闭环性能的影响；设计权值更新律与触发机制，实现对未知非线性/不确定项的在线补偿并提升鲁棒性。” Card 3 header: “具体目标3（G3）” Body: “面向模型信息不完备与参数不确定等情形，研究模型弱依赖的学习辨识–事件触发一致性控制方法；给出一致性误差收敛性与有界性条件，以及DRNN权值与内部递归状态的有界性结论；提升方法的工程可实施性与适用范围。” Bottom bar text: “预期形成：低通信、高可靠、可验证的一致性控制理论与方法体系” Negative prompt Avoid photorealism, avoid dense paragraphs, avoid tiny illegible text, avoid complex mathematical derivations, avoid cluttered decorative elements.

Neuronal cells depicted as a catalog of diferent colors

wide horizontal banner for the Data Science Club at NIST University, modern and professional university design. NIST University logo placed clearly on the left side (official, flat vector style, accurate proportions). Futuristic data science visuals including data charts, AI neural networks, machine learning icons, code snippets, and flowing data lines. Color palette: deep blue, white, cyan, and dark gray with subtle gradients. Bold, clean typography with main text: “Data Science Club – NIST University” centered or right-aligned. Minimal, high-tech academic style suitable for website headers and campus displays. High resolution, sharp details, professional branding, no clutter.

Create a BioRender-style vector infographic. Place the panel header “研究目标” as a small caption in the upper-left corner (not a large title). Set its font size to ~80% of the main box titles, use regular weight (not bold), and keep it visually subtle. Use a clean professional layout, flat colors, thick outlines, minimal shadows, and consistent sans-serif font (Microsoft YaHei). Canvas: 16:9 or 16:10 landscape. The figure should be non-technical (no equations), focusing on goals hierarchy. All text inside boxes must be Chinese exactly as provided. Layout Split the figure into two parts: Left Part (Overall Goals Pyramid): Draw a 3-layer stacked pyramid (or three stacked rounded rectangles) labeled from top to bottom: “理论目标”, “方法目标”, “应用目标”. Use subtle distinct colors for each layer. Add a small left-side label “总体目标” above the pyramid. Right Part (Three Specific Objectives Cards): Place three numbered rounded-rectangle cards vertically aligned (G1, G2, G3). Each card contains the specific objective text. Draw thin arrows from each card to the related pyramid layer(s): G1 arrows to “理论目标” (primary) and slightly to “方法目标” (secondary) G2 arrows to “方法目标” (primary) and slightly to “理论目标” (secondary) G3 arrows to “应用目标” (primary) and slightly to “方法目标” (secondary) Bottom Bar (One-line Summary): Add a wide rounded rectangle at the bottom spanning the width, labeled “预期形成：低通信、高可靠、可验证的一致性控制理论与方法体系”. Icons (small, minimal, optional) Next to “理论目标”: a Lyapunov/analysis icon (V(x)) Next to “方法目标”: a neural network + event-trigger clock icon Next to “应用目标”: drone + robot icons Next to bottom bar: a balance scale icon labeled “性能—通信—能耗” Chinese text to place (exact) Overall Goals (left pyramid): Top layer title: “理论目标” Text: “建立一致性误差动力学描述与统一的收敛/有界性分析框架；给出误差判据、误差上界、无Zeno与IET下界等可验证结论。” Middle layer title: “方法目标” Text: “提出以DRNN为核心的学习辨识器、动态事件触发机制与分布式控制协议协同设计方法；揭示触发参数、拓扑、辨识误差与一致性性能的定性—定量关系；建立‘一致性性能—通信资源与能耗开销’权衡机制。” Bottom layer title: “应用目标” Text: “依托多无人机与多机器人平台开展仿真与实验验证；形成可推广的低通信、高可靠、可验证协同控制方法；服务无人系统集群、网络化制造单元及分布式能源等场景。” Right cards (specific objectives): Card 1 header: “具体目标1（G1）” Body: “构造显式利用触发区间信息的looped-functional型Lyapunov分析工具，降低收敛性与有界性结论的保守性；推导误差上界、无Zeno条件与IET下界；刻画IET与拓扑结构、触发参数及系统状态之间的定性—定量关系。” Card 2 header: “具体目标2（G2）” Body: “建立基于DRNN学习辨识的分布式事件触发一致性控制框架；研究学习误差、触发误差与拓扑耦合误差对闭环性能的影响；设计权值更新律与触发机制，实现对未知非线性/不确定项的在线补偿并提升鲁棒性。” Card 3 header: “具体目标3（G3）” Body: “面向模型信息不完备与参数不确定等情形，研究模型弱依赖的学习辨识–事件触发一致性控制方法；给出一致性误差收敛性与有界性条件，以及DRNN权值与内部递归状态的有界性结论；提升方法的工程可实施性与适用范围。” Bottom bar text: “预期形成：低通信、高可靠、可验证的一致性控制理论与方法体系” Negative prompt Avoid photorealism, avoid dense paragraphs, avoid tiny illegible text, avoid complex mathematical derivations, avoid cluttered decorative elements.

Draw a simplified neuron with computational and mathematical components. Do not add letters, and use material design style. Use a white background. Consider nodes of the networks using black and dark gray.

Draw a simplified neuron with computational and mathematical components. Do not add letters, and use material design style.

Network where circles are black (or gray) and the background is white.

Artistic representations of AI or machine learning concepts.

Draw an illustration for the chapter "How to start working with neural networks: a step-by-step guide". I suggest an idea: Draw a man with a beard and mustache, who, raising his hand to his chin, thoughtfully looks up at the list of neural networks. The style is cartoon flat illustration. Background color - Yellow - #fee6c2

A stylized neural network overlay over a dynamic stock market graphic, combined with symbols for artificial intelligence, financial data and global networking. Style: tech-inspired, visually powerful, with bright lines on a dark background.

A detailed 3d render of a network of neurons, sharp focus, depth of field, neon, 8k, photo realism, --ar 3:4, furry-painting art

Draw a simplified neuron with computational and mathematical components

neural network connections of a brain made of electronic circuit tracks --ar 16:9

A detailed 3d render of a network of neurons, sharp focus, depth of field, neon, 8k, photo realism, --ar 3:4, painterly digital painting

A detailed 3d render of a network of neurons, sharp focus, depth of field, neon, 8k, photo realism, --ar 3:4, painterly digital painting:2

Draw an illustration for the chapter "How to start working with neural networks: a step-by-step guide". I suggest an idea: Draw a man with a beard and mustache, who, raising his hand to his chin, thoughtfully looks up at the list of neural networks.