The absolutely beautiful free hands-on book "Physics-Based Deep Learning Book" by Thuerey et al. (2026) is now in @ChapterPal's collection. The textbook is an applied guide to physics-based deep learning, also known as scientific machine learning, which blends numerical physical simulations with modern artificial intelligence architectures. Designed for researchers, engineers, and students with a working knowledge of deep learning fundamentals and basic partial differential equations, the book focuses on practical implementations using deep learning frameworks such as PyTorch, JAX, and the differentiable simulation library ΦFlow. The textbook, at 350 pages, has 163 illustrations and 39 hands-on coding exercises. Read the book with an AI tutor: chapterpal.com/book/4d08ce3b… All books on ChapterPal are free to read with a free account. Table of contents: Chapter 1: Introduction - Welcome ... * Coming up * Comments and suggestions * Thanks! * Citation * Time to get started - A Teaser Example * Differentiable physics * Finding the inverse function of a parabola * A differentiable physics approach - A Probabilistic Generative AI Approach * Discussion * Next steps - Overview * Motivation * Categorization * Looking ahead * Implementations - Models and Equations * Deep learning and neural networks * Partial differential equations as physical models * Some example PDEs * Preliminaries * Newton's method * Approximating the Hessian * Broyden's method * BFGS * Gauss-Newton * Gradient Descent Chapter 2: Neural Surrogates and Operators - Supervised Training * Problem setting Chapter 3: Physical Losses - Physical Loss Terms * Using physical models Chapter 4: Differentiable Physics - Introduction to Differentiable Physics * Differentiable operators * Jacobians * Learning via DP operators * A practical example * Backpropagation through solver steps * Alternatives: noise * Complex examples - Reducing Numerical Errors with Neural Operators * Problem formulation * Getting started with the implementation * Simulation setup * Network and transfer functions * Training setup * Interleaving simulation and NN * Test evaluation * Next steps - Solving Inverse Problems with NNs * Formulation * Control of incompressible fluids * Data generation * Supervised initialization * CFE pretraining with differentiable physics * End-to-end training with differentiable physics * Next steps - Discussion of Differentiable Physics * Integration * Reducing data shift via interaction * Generalization Chapter 5: Probabilistic Learning - Introduction to Probabilistic Learning * Uncertainty * Forward or Backward? * Simulation-based Inference - Learning a Probability Distribution * Fundamentals: A Training Objective * From Unconditional to Conditional * Learning Distributions with Normalizing Flows * Practical Example: Learning Gaussians * A Simple Normalizing Flow based on Affine Couplings * Neural ODEs: Making Normalizing Flows Continuous * Summary of Normalizing Flows - Score Matching * Gaussian Toy Dataset with Analytic Scores * Learning the Score * Langevin Dynamics * Full Denoising Algorithm * Training with DDPM - Flow Matching * Learning Flows with Velocities * Mappings and Conditioning * Score Matching with Differentiable Physics * Summary of Physics-based Diffusion Models - Probabilistic Inverse Problem with Differentiable Simulations * Toy Problem setup * Conditioning * Implementation * Backbone Network Definition * Variance Schedule * Diffusion Model Definition * Training * Test Dataset * Test Inference * Accuracy of the Prediction * Summarizing Time Predictions with Diffusion Models - Unconditional Stability * Main Considerations for an Evaluation * Comparing Architectures * Stability Criteria * Batch Size vs Rollout * Summary - Graph-based Diffusion Models * Diffusion Graph Net (DGN) * Diffusion on Graphs * Diffusion in Latent Space * Turbulent Flows around Wings in 3D * Distributional accuracy * Computational Performance - Distributional Accuracy of Diffusion Graph Nets * Implementation * Sample-wise Accuracy * Evaluating Distributional Accuracy - Discussion of Probabilistic Learning Chapter 6: Reinforcement Learning - Introduction to Reinforcement Learning Chapter 7: Improved Gradients - Scale-Invariance and Inversion * The crux of the matter * Traditional optimization methods * Quasi-Newton methods * Inverse simulators * NN training * Loss functions * Iterations and time dependence * SIP training in action * Discussion of SIP Training - Learning to Invert Heat Conduction with Scale-invariant Updates * Problem Statement * Implementation * Data generation * Differentiable physics and gradient descent * Stable SIP gradients * Neural network and loss function * Training * Evaluation * Next steps - Half-Inverse Gradients * Derivation Chapter 8: Fast Forward Topics - Additional Topics - Model Reduction and Time Series * Reduced order models * Time series * End-to-end training * Source code - Unstructured Meshes and Meshless Methods * Types of computational meshes * Unstructured meshes and graph neural networks * Meshless and particle-based methods * Continuous convolutions * Learning the dynamics of liquids * Source code - Generative Adversarial Networks * Maximum likelihood estimation Chapter 9: Outlook - Outlook * Some specific directions * Closing remarks References Notation and Abbreviations - Math notation: - Summary of the most important abbreviations:

The "High-Level System Design Handbook" by Aayush Soni (2026) is now in @ChapterPal's collection of free books. The book is a comprehensive guide intended for software engineers, system architects, and technical leaders who design, scale, and maintain large-scale distributed platforms or prepare for technical design interviews. The text assumes a baseline familiarity with programming, basic operating system mechanics such as processes and threads, fundamental data structures, relational database concepts, and networking protocols such as TCP and HTTP. From this foundation, the book covers the full architectural lifecycle of distributed software, ranging from single-machine performance limits to planet-scale multi-region topologies and modern artificial intelligence platforms. Read the book with an AI tutor: chapterpal.com/book/9bc8434f… All book on ChapterPal are free to read with a free account. The table of contents: Here’s the ToC with all level-three headings removed. Chapter 0: Prerequisites * 0.0 Networking Fundamentals for System Design * 0.1 Operating System Essentials for System Design * 0.2 Data Structures for Distributed Systems * 0.3 Database Fundamentals for System Design * 0.4 API Design Basics: REST, GraphQL, gRPC, and the Hard Parts * References Chapter 1: Core Fundamentals * 1.0 Scalability: Growing a System Without Breaking It * 1.1 Latency and Throughput: The Two Numbers That Matter * 1.2 Availability and Reliability: Nines, SLOs, and Staying Up * 1.3 Consistency Models: What Readers Actually See * 1.4 Back-of-the-Envelope Estimation * 1.5 How to Approach a System Design Question * 1.6 Trade-off Thinking * References Chapter 2: Building Blocks * 2.0 Load Balancers: Spreading Traffic, Absorbing Failure * 2.1 Reverse Proxies and API Gateways: The Smart Edge * 2.2 Content Delivery Networks: Moving Bytes Closer to Users * 2.3 Caching: From Browser to Database * 2.4 SQL Databases: The Boring Technology That Wins * 2.5 NoSQL Databases: Picking the Right Non-Relational Tool * 2.6 Database Partitioning and Sharding: When One Node Is Not Enough * 2.7 Database Replication: Keeping Copies in Sync * 2.8 Message Queues and Streaming: Decoupling at Scale * 2.9 Pub/Sub: Fan-Out and Event-Driven Systems * 2.10 Real-Time Communication: WebSockets, SSE, and Long Polling * 2.11 Rate Limiting: Protecting Systems from Themselves * 2.12 Service Discovery and Service Mesh: Finding and Talking to Services * 2.13 Blob and Object Storage: Storing the Big Stuff * 2.14 Geospatial Indexing: Geohash, Quadtree, R-tree, S2, and H3 * 2.15 Edge Computing (Cloudflare Workers, Lambda@Edge, Deno Deploy) * References Chapter 3: Distributed Systems Theory * 3.0 Consensus Protocols: How Distributed Systems Agree * 3.1 Consistency Deep Dive: Linearizability, Serializability, and the Spectrum Between * 3.2 Quorums and Replication: The Math of R + W > N * 3.3 CAP and PACELC: The Tradeoff That Keeps Confusing People * 3.4 Clocks and Ordering: Lamport, Vector, and Hybrid Logical Clocks * 3.5 CRDTs: Conflict-Free Replicated Data Types * 3.6 Distributed Transactions: 2PC, Saga, and When to Avoid Both * 3.7 Idempotency and Exactly-Once: The Honest Truth About Delivery Guarantees * 3.8 Failure Detection: Deciding a Node Is Dead * 3.9 Consistent Hashing: Keys to Nodes Without Global Reshuffles * 3.10 Merkle Trees and Anti-Entropy: Keeping Replicas in Sync Cheaply * References Chapter 4: Data Systems * 4.0 Storage Engines: B-Trees, LSM-Trees, and Why Your Database Feels the Way It Does * 4.1 OLTP vs OLAP: Row Stores, Column Stores, and Matching Shape to Workload * 4.2 Data Warehouses and Data Lakes: Structure, Schema, and the Lakehouse * 4.3 Stream vs Batch Processing: Lambda, Kappa, and the End of That Debate * 4.4 Change Data Capture: Streaming the Database's Inner Monologue * 4.5 Search Systems: Inverted Indexes, BM25, and Running Elasticsearch in Production * 4.6 Time-Series Databases: Metrics, Events, and Retention at Scale * 4.7 Graph Databases: Property Graphs, Cypher, and When Joins Are the Problem * 4.8 Vector Databases: Embeddings, ANN Indexes, and the Retrieval Layer for AI * 4.9 Key-Value Stores: Redis, Memcached, DynamoDB, and Picking the Right Hash Table * References Chapter 5: Architecture Patterns * 5.0 Monolith vs Microservices: Team Topology, Conway's Law, and the Distributed System Tax * 5.1 Event-Driven Architecture: Notifications, State Transfer, and Choreography * 5.2 CQRS: Separating Reads from Writes Without Losing Your Mind * 5.3 Event Sourcing: Events as the Source of Truth * 5.4 Serverless: Functions, Cold Starts, and When FaaS Actually Saves Money * 5.5 Backend for Frontend: Per-Client API Aggregation Done Right * 5.6 Strangler Fig: Incremental Migration Without a Big Bang * 5.7 Hexagonal and Clean Architecture: Keeping Business Logic Independent * 5.8 Multi-Region Architecture: Active-Passive, Active-Active, and CRDTs * 5.9 Multi-Tenancy: Silo, Pool, and the SaaS Isolation Spectrum * 5.10 CRDT Applications (Yjs, Automerge, Local-First Software) * References Chapter 6: Reliability & Operations * 6.0 Observability: Metrics, Logs, Traces, and the OpenTelemetry Standard * 6.1 SLI, SLO, SLA, and Error Budgets: Making Reliability Quantitative * 6.2 Resilience Patterns: Timeouts, Retries, Circuit Breakers, and Bulkheads * 6.3 Graceful Degradation: When Partial Service Beats No Service * 6.4 Auto-Scaling and Capacity Planning: From HPA to Predictive Scaling * 6.5 Deployment Strategies: Blue-Green, Canary, Rolling, and Feature Flags * 6.6 Chaos Engineering: Breaking Things on Purpose * 6.7 Incident Management: From Detection to Blameless Postmortem * 6.8 Health Checks and Readiness: Telling the Truth About Whether You're Up * 6.9 Cost Optimization and FinOps * 6.10 Platform Engineering: IDPs, Golden Paths, and DX * References Chapter 7: Security at Scale * 7.0 Authentication vs Authorization: Identity, Permissions, and Access Models * 7.1 OAuth 2.0 and OpenID Connect: Delegated Authorization and Identity Done Right * 7.2 JWT Deep Dive: Signed Tokens, Claims, and the Revocation Problem * 7.3 mTLS and Service-to-Service Authentication: SPIFFE, Service Mesh, and Zero Trust * 7.4 Secrets Management: Vault, KMS, and the End of Secrets in Config Files * 7.5 DDoS Protection and WAFs: Mitigating Volumetric and Application Attacks * 7.6 Data Residency and Compliance Architecture (GDPR, DPDP, CCPA, Right-to-Erasure) * 7.7 Supply Chain Security: SBOM, SLSA, Sigstore, and Defending Against xz-utils * 7.8 Privacy-Preserving Systems (Differential Privacy, Federated Learning) * 7.9 Post-Quantum Cryptography: Migrating to ML-KEM, ML-DSA, and a Crypto-Agile Future * References Chapter 8: Case Studies * 8.0 Design a URL Shortener (TinyURL / bit.ly) * 8.1 Design a Pastebin (Paste Sharing Service) * 8.2 Design a Distributed Rate Limiter * 8.3 Design a Distributed Key-Value Store (Dynamo / Cassandra / Riak) * 8.4 Design a Notification System (Push, SMS, Email at Scale) * 8.5 Design a Chat System (WhatsApp / Messenger / Signal) * 8.6 Design a Social Media Feed (Twitter / Instagram / LinkedIn) * 8.7 Design a Photo Sharing Service (Instagram) * 8.8 Design a Web Crawler (Googlebot-style) * 8.9 Design Search Autocomplete (Typeahead Suggestions) * 8.10 Design a Video Streaming Service (YouTube / Twitch / TikTok) * 8.11 Design Netflix (End-to-End) * 8.12 Design a Ride-Hailing Service (Uber / Lyft) * 8.13 Design Google Maps (Routing and Tile Rendering) * 8.14 Design a File Sync Service (Dropbox / Google Drive) * 8.15 Design Collaborative Editing (Google Docs / Figma / Notion) * 8.16 Design a Distributed Cache (Memcached / Redis Cluster) * 8.17 Design a Recommendation System (Netflix / YouTube / TikTok) * 8.18 Design a Ticketing System (BookMyShow / Ticketmaster) * 8.19 Design a Payment System (Stripe / PayPal) * 8.20 Design a Stock Exchange (Matching Engine) * 8.21 Design a Food Delivery Service (DoorDash / Swiggy) * 8.22 Design a Metrics Pipeline (Prometheus / InfluxDB / Thanos) * 8.23 Design Ad-Click Aggregation (Real-Time Stream Processing) * 8.24 Design a Logging Platform (ELK / Loki / Splunk) * 8.25 Design a Proximity Service (Nearby Friends / Yelp) * 8.26 Design a Real-Time Leaderboard * 8.27 Design a Unique ID Generator (Snowflake, ULID, TSID, UUIDv7) * 8.28 Design a Hotel Reservation System (Booking.com / Airbnb) * 8.29 Design a Distributed Job Scheduler (Airflow / Temporal / Distributed Cron) * 8.30 Design ChatGPT (Conversational AI at Scale) * 8.31 Design an Enterprise RAG System * 8.32 Design a Coding Agent (Claude Code / GitHub Copilot / Cursor) * 8.33 Design Perplexity (AI Search with Citations) * 8.34 Design a Voice Agent (Alexa / Siri-Class Realtime) * 8.35 Design a Content Moderation System at Scale * 8.36 Design a Semantic Cache for LLM Applications * 8.37 Design a Model Router and Gateway (OpenRouter / LiteLLM) * 8.38 Design a Feature Flag Service (LaunchDarkly / Harness FME / Unleash) * 8.39 Design a DNS Service (Cloudflare 1.1.1.1 / Google 8.8.8.8) * 8.40 Design a Dating App (Tinder / Hinge / Bumble) * 8.41 Design an Online Auction (eBay / Catawiki) * 8.42 Design a Multi-Tenant SaaS Platform * 8.43 Design a Video Conferencing System (Zoom / Google Meet) * 8.44 Design an Email Service at Gmail Scale (1.8B Users, 300B Messages/Day) * 8.45 Design Live Comments at Scale (FB Live / YouTube Live / Twitch Chat) * 8.46 Design a Fraud Detection System (Stripe Radar / PayPal / Feedzai) * 8.47 Design a Fitness Tracking Service (Strava / MapMyRun) * 8.48 Design an Online Judge (LeetCode / Codeforces / HackerEarth) * 8.49 Design a Price Tracking Service (CamelCamelCamel / Honey / Keepa) * 8.50 Design an API Gateway at Scale (Kong / AWS API Gateway / Apigee / Envoy) * 8.51 Design a CI/CD Platform (GitHub Actions / GitLab CI / CircleCI) * 8.52 Design an Observability Platform (Datadog / New Relic / Honeycomb) * 8.53 Design a Search Engine (Google-Scale / Brave Search) * 8.54 Design a Brokerage Platform (Robinhood / E*TRADE / Interactive Brokers) * 8.55 Design Channel-Scale Chat (Discord / Slack) * References Chapter 9: AI & ML System Design * 9.0 LLM Serving Architecture (vLLM, TGI, TensorRT-LLM) * 9.1 RAG Pipelines (Retrieval-Augmented Generation) * 9.2 Vector Search at Scale (HNSW, IVF-PQ, DiskANN) * 9.3 AI Agent Architectures (ReAct, Reflection, Planning, Tool Use, Memory) * 9.4 Multi-Agent Orchestration (LangGraph, OpenAI Agents SDK, AutoGen, Swarm) * 9.5 LLM Evaluation and Observability (Ragas, LangSmith, TruLens, LLM-as-Judge) * 9.6 LLMOps and Prompt Engineering (Versioning, Guardrails, Red-Teaming) * 9.7 LLM Cost Optimisation (Semantic Cache, Model Routing, Cascading, Prompt Caching) * 9.8 LLM Safety and Guardrails (OWASP LLM Top 10, Prompt Injection, PII, Jailbreaks) * 9.9 ML System Design Fundamentals * 9.10 Feature Stores and Model Serving (Feast, Tecton, KServe, BentoML, MLflow) * 9.11 Recommendation Systems Deep Dive (DLRM, Two-Tower, Embedding Retrieval, Cold Start) * 9.12 Realtime AI and Voice Agents (Streaming Inference, WebRTC, LiveKit, Deepgram) * 9.13 Multimodal AI Systems (CLIP, Whisper, LayoutLM, Document AI) * 9.14 Data Infrastructure for AI (Embedding Pipelines, Chunking, Unstructured ETL, MCP) * References Chapter 10: Emerging Patterns * 10.0 Green Computing (Carbon-Aware Scheduling, PUE, Sustainable Systems) * References Chapter 11: Interview Framework * 11.0 Interview Frameworks Compared (RESHADED, PEDALS, ADEPT) * 11.1 Requirements Scoping: Functional, Non-Functional, and MoSCoW * 11.2 Diagramming Skills for System Design Interviews * 11.3 Trade-off Articulation: Saying 'It Depends' Well * 11.4 Company-Specific Interview Flavors (Amazon, Google, Meta, Netflix) * 11.5 Design Doc Authoring: RFCs, ADRs, and the Staff Engineer's Written Output * References Trade-offs Library * 1. Strong vs Eventual Consistency * 2. ACID vs BASE * 3. SQL vs NoSQL * 4. Latency vs Throughput * 5. CAP and PACELC Applied * 6. Cache Strategies: Cache-Aside vs Write-Through vs Write-Behind * 7. Batch vs Stream Processing * 8. Load Balancer vs Reverse Proxy vs API Gateway * 9. REST vs gRPC vs GraphQL * 10. Polling vs Long-Polling vs SSE vs WebSockets vs Webhooks * 11. Rate Limiting Algorithms: Token Bucket vs Sliding Window * 12. Optimistic vs Pessimistic Concurrency Control * 13. Partitioning Schemes: Range, Hash, Consistent Hash, Directory * 14. B-tree vs LSM-tree Storage * 15. Monolith vs Microservices * 16. Replication Topologies: Leader-Follower, Multi-Leader, Leaderless * 17. Distributed Transactions: 2PC vs Saga vs TCC * 18. Push vs Pull (Fan-out, Messaging, Feed) * 19. Lambda vs Kappa Architecture * 20. Vertical vs Horizontal Scaling * 21. Normalization vs Denormalization * 22. Single-Region vs Multi-Region Deployment * References