✈️🧮 𝙋𝙧𝙤𝙗𝙖𝙗𝙞𝙡𝙞𝙨𝙩𝙞𝙘 𝙊𝙥𝙩𝙞𝙢𝙞𝙨𝙖𝙩𝙞𝙤𝙣 𝙤𝙛 𝘾𝙤𝙢𝙥𝙤𝙨𝙞𝙩𝙚 𝙎𝙩𝙧𝙪𝙘𝙩𝙪𝙧𝙚𝙨: 𝙈𝙖𝙘𝙝𝙞𝙣𝙚 𝙇𝙚𝙖𝙧𝙣𝙞𝙣𝙜 𝙛𝙤𝙧 𝘿𝙚𝙨𝙞𝙜𝙣 𝙊𝙥𝙩𝙞𝙢𝙞𝙨𝙖𝙩𝙞𝙤𝙣
How do we design lighter, safer, more reliable aircraft composite structures—while accounting for real-world uncertainties without drowning in computational cost?
📘 By Kwangkyu Alex Yoo, Omar Bacarreza, and M H Ferri Aliabadi, the book introduces an innovative multi-fidelity probabilistic optimisation approach that balances statistical reliability with computational efficiency.
Volume 15 of the 𝘊𝘰𝘮𝘱𝘶𝘵𝘢𝘵𝘪𝘰𝘯𝘢𝘭 𝘢𝘯𝘥 𝘌𝘹𝘱𝘦𝘳𝘪𝘮𝘦𝘯𝘵𝘢𝘭 𝘔𝘦𝘵𝘩𝘰𝘥𝘴 𝘪𝘯 𝘚𝘵𝘳𝘶𝘤𝘵𝘶𝘳𝘦𝘴 series, this book combines high- and low-fidelity models—with machine learning bridging the gaps—to deliver robust design solutions for aircraft composites under thermo-mechanical conditions.
🔎 𝐖𝐡𝐲 𝐭𝐡𝐢𝐬 𝐛𝐨𝐨𝐤 𝐢𝐬 𝐞𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥 𝐫𝐞𝐚𝐝𝐢𝐧𝐠:
📖 1. Foundations of Structural Optimisation
🧱 Fundamentals of structural optimisation under uncertainty
⚙️ Composite materials and their behaviour in aerospace structures
🎯 The case for probabilistic design in safety-critical systems
🔀 2. Multi-Fidelity Modelling
🏔️ Combining high- and low-fidelity models for efficient exploration
🧠 Artificial neural networks and nonlinear autoregressive Gaussian processes
🌉 Machine learning as the bridge between model fidelities
🛡️ 3. Reliability-Based Design Optimisation
📊 Multi-fidelity reliability-based design optimisation frameworks
🎲 Accounting for uncertainty in material properties and geometry
✅ Achieving statistically reliable solutions at reduced cost
💪 4. Robust Design Optimisation
🔁 Multi-fidelity robust design with successive high-fidelity correction
📈 Balancing performance with resilience to variability
🏗️ Demonstrations on aircraft composite structures
📉 5. Sparse Data and Practical Applications
🗂️ Probabilistic optimisation with sparse high-fidelity information
✈️ Applications across aerospace, automotive, civil, wind energy, and offshore industries
🧪 Case studies demonstrating accuracy and computational savings
🌐 Explore the book here:
worldscientific.com/worldsci…
💡 Ideal for undergraduate and postgraduate students in aerospace, mechanical, and design engineering, as well as professional engineers and researchers across aircraft, automotive, civil engineering, wind energy, offshore oil and gas, and naval architecture industries.
👉 Quote 𝐖𝐒𝐓𝐖𝐓𝐑𝟑𝟎 at checkout to enjoy 𝟑𝟎% 𝐨𝐟𝐟 your purchase now!
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